=Paper=
{{Paper
|id=Vol-3723/paper2
|storemode=property
|title=Ensuring accuracy of personal data processed in blockchain systems
|pdfUrl=https://ceur-ws.org/Vol-3723/paper2.pdf
|volume=Vol-3723
|authors=Olexander Belej,Yulian Fedirko,Oleksandr Markelov
|dblpUrl=https://dblp.org/rec/conf/modast/BelejFM24
}}
==Ensuring accuracy of personal data processed in blockchain systems==
https://ceur-ws.org/Vol-3723/paper2.pdf
Ensuring accuracy of personal data processed in blockchain
systems
Olexander Belej1,*,†, Yulian Fedirko1,† and Oleksandr Markelov1,†
1 Lviv Polytechnic National University, 5 Mytropolyt Andrei str., Building 4, Room 324, Lviv 79013, Ukraine
Abstract
Based on the analysis of the existing requirements and methods of ensuring data security, the
relevance of developing a method of ensuring data security during processing in blockchain
systems by using artificial neural networks has been confirmed. A method of ensuring the
reliability of personal data processed in blockchain systems has been developed. As part of the
development of this method, the category of methods for ensuring data reliability was expanded
by using artificial neural networks to identify unreliable personal data when they are entered
into the blockchain system. A method of analyzing the authorization behavior of information
system users has been developed. As part of the development of this method, user behavior was
formalized and the possibility of detecting anomalies in user behavior using artificial neural
networks was demonstrated.
Keywords
data security, blockchain systems, personal data, reliability, identification, artificial neural
networks 1
1. Introduction
Since 2009, information systems based on blockchain technology have been gaining
more and more popularity. Blockchain is a data processing technology based on the
following basic principles: a data storage structure is a blockchain containing information
built according to certain rules; each block in the chain uses the hash value of the previous
block. This information applies to the nearest block;
Blockchain consists of the following basic elements: "useful" service data from the
previous block in the chain; "Useful" data can be any information that needs distributed
storage. For example, additional information may include the time the block was created,
its computational complexity, and the random number used to calculate the hash. The hash
MoDaST-2024: 6th International Workshop on Modern Data Science Technologies, May, 31 - June, 1, 2024, Lviv-
Shatsk, Ukraine
∗ Corresponding author.
† These authors contributed equally.
Oleksandr.I.Belei@lpnu.ua (O. Belej); Yulian.A.Fedirko@lpnu.ua (Yu. Fedirko);
Oleksandr.E.Markelov@lpnu.ua (O. Markelov)
0000-0003-4150-7425 (O. Belej); 0000-0001-9968-7313 (Yu. Fedirko); 0000-0002-2432-0768 (O.
Markelov)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
sum of the previous block is used to uniquely order the blocks. The exception is the hash
sum of the previous block specified in the genesis block, which is usually randomly
generated. The hash of the current block verifies the information contained in that block
and relates it to the next block in the chain. In general, blockchain works like Figure 1. With
the development of blockchain technology, its basic principles are also constantly evolving
and changing.
To solve these problems, new blockchain privacy solutions are constantly emerging
based on cryptographic privacy technology, which provides users with a mechanism of
anonymity and control over their data when conducting any digital transaction on the
ledger, following the principle of self-verification. Protected Sovereign Identity (PSI)
template [1]. The author of [2] evaluated Acce-chain through experiments, and the results
showed that the coverage performance is feasible under real VEC settings, and the query
efficiency can be several orders of magnitude better than the baseline.
The proposed approach covers all aspects of the national health insurance scheme and
therefore allows making changes to existing procedures without changing the rules of the
health insurance system [3]. The author of [4] used Remix IDE to rigorously test the smart
contract code in various scenarios and referenced the code on GitHub. Blockchain verifies
the authenticity of IoT devices and cloud service providers added to the network and
provides a mechanism to manage IoT data access policies. In addition, a prototype of the
proposed framework was implemented using Hyperledger Fabric and Intel SGX, and an
analysis of blockchain and SGX performance was also presented [5].
In [6] authors proposed an incentive mechanism to assess the value of publishers’ efforts
in managing and maintaining research data and creating new blocks. The results
demonstrate the effectiveness of the proposed system in managing large datasets with low
latency. This paper [7] proposes a new incentive mechanism that uses university degrees
to save academic records and create new blocks. We conduct large-scale experiments to
evaluate the performance of UniChain, and the results show the effectiveness of the
proposal in processing large data sets with low latency.
The author [7] proposed a maritime transport communication system that supports the
Internet of Things. The system is a decentralized system consisting of base stations and sea
buoys. Agricultural insurance can help smallholder farmers in developing countries manage
risks that they cannot manage on their own [8].
The study [10] examines the needs and prospects of using blockchain technology in the
Internet of Things. Consider implementing encryption for open, decentralized IoT systems
not to restrict viewing, but to limit unauthorized access. By integrating hash functions and
digital signatures into the blockchain itself, it demonstrates the ability to protect data from
unauthorized access. This is achieved using an encryption algorithm based on a pseudo-
random number generator.
This study [11] is based on the above facts and aims to explore how to make blockchain
GDPR compliant. As such, it contains several proposals to make blockchain technology more
GDPR compliant.
In the article [12], the authors describe the design of a system for the deployment and
processing of survey data following the GDPR. It combines the Hyperledger Fabric
blockchain for data immutability and the InterPlanetary File System (IPFS) for storage.
Paper [13] developed a healthcare system that can securely manage personal medical data
and create interactions between doctors, patients, insurance companies, and pharmacies or
medical stores.
Today, blockchain technology can be conditionally divided into five generations.
The first generation of blockchain (Blockchain 1.0) is the basis of digital payment
systems, the first and most popular representative of which is Bitcoin, launched in 2009.
One of the main disadvantages of Bitcoin is the hash calculation method. Since the task
of calculating the hash value is solved in a decentralized manner (which is good because it
increases the reliability of the chain), then only one calculation participant (miner) can
become the winner. Therefore, most of the miner's work is wasted, because... the
calculations performed are useless. As of December 2021, the total computational power of
Bitcoin miners is approximately 174 petaflops per second. This leads to another
disadvantage - the tendency to centralize calculations. In the past, individual miners may
have been the winners, but today, as the total computing power of miners increases, so does
the computational complexity, and the only way to calculate hashes (and get rewarded) is
faster than other methods. Just unite the miners. According to a report by digital asset
management company CoinShares, as of June 12, 2021, approximately 65% of the effective
computing power of cryptocurrency mining equipment is concentrated in China.
Blockchain of the second generation (Blockchain 2.0) not only supports the functions of
registration, confirmation, and transfer of currency but also supports other types of assets
- various contracts and properties. Second-generation blockchain protocols can use
Bitcoin's decentralized ledger or create their decentralized ledger (Figure 1).
Figure 2: Blockchain diagram based on current block computing power: [10].
The areas of application of second-generation blockchain technology can be divided into
intellectual assets; as part of this effort, it seems appropriate to clarify the basic information
about smart assets and smart contracts. Intellectual property rights allow you to trade any
property. After assets are registered in the decentralized ledger, control of the property is
transferred to the key holder. Transfer of private keys means transfer of ownership.
The general meaning of smart contracts comes from the idea of smart assets. Smart
contracts are a method of conducting transactions in a decentralized ledger based on the
use of cryptocurrencies and smart assets to sign agreements through blockchain
technology. An example of a smart contract is a transaction that remains inactive in the
decentralized ledger until a certain date or event: the transfer of inheritance rights on the
day of the death of the owner of the asset, the purchase or sale of an asset on the day of the
death of the owner of the asset. date of death of ownership. date. If the owner of the asset
dies, the exchange takes place after notification and ownership automatically pass from the
finance company to the individual after all loans are paid off. Procedure from the point of
view of judicial practice, high-quality contract drafting, and the introduction of automatic
enforcement mechanisms can significantly reduce the number of disputes. The combined
use of smart assets and smart contracts can create a lending system that uses the borrower's
smart assets as collateral, thereby reducing the cost of insurance against fraud and abuse
and making lending safer and more profitable. A distinctive feature of smart contracts is
that there is no need for trust between participants — smart contracts are executed
automatically using code running on blockchain technology, leaving no room for the human
factor. However, the use of smart contracts today requires a strict regulatory framework to
regulate the procedures for fulfilling contractual obligations.
One of the key directions in the development of third-generation blockchain technology
is the use of methods based on directed acyclic graphs (DAG). A directed acyclic graph is a
topology tree-based data processing structure. The arrangement of blocks in this structure
does not have to be contiguous and provides direct communication between any
transactions on the chain. The chain in this structure is not built by blocks but by
transactions. The hash value is calculated from the parent transaction and passed to the
next related transaction. The main advantages of using direct acyclic graphs are speed, ease
of growth, and increased security of data processing systems. In the first generation of
blockchains, it took about 10 minutes to create a new block. Creating a second-generation
blockchain takes just 20 seconds. When using a direct acyclic graph, there is no need to
collect transactions into blocks, and theoretically, hundreds of thousands of transactions
per second can be guaranteed. The developers of blockchain-based systems refuse to avoid
the high complexity of hash calculations, which leads to the need not to organize miners into
mining pools, which in turn leads to a more decentralized network and therefore higher
profits.
In general, third-generation blockchains, regardless of whether direct acyclic graphs are
used or not, can solve the currency-independent problems of market transactions.
Examples of such solutions include:
email security system KeyID, a system that combines 32-bit alphanumeric
identification codes with human-readable names called OneName and BitID, a
system that identifies Bitcoin wallet addresses based on the Bihandle type;
provision of services for authentication of full documentary confirmations
(regarding confirmation of authenticity of wills, contracts, powers of attorney,
medical certificates, promissory notes, etc.) without disclosing the information
contained in them;
a personalized government that provides instant cryptocurrency payments for
active PR and commissions for event organizers;
control of some traditional public services;
an identification system that provides people with foreign passports that are not tied
to a specific country;
WikiLeaks and Twitter document solutions to combat online censorship.
One of the technical solutions based on fifth-generation blockchain technology is the
Telegram Open Network (TON) project. TON is a platform for creating a blockchain
ecosystem that provides storage of personal data in cloud storage and registration in
services that require authentication. TON consists of the following main elements:
1. Blockchain is the main component of TON;
2. TON network - provides communication between all TON components;
3. Services, services, and applications Platform that provides services for TON
applications;
4. TON Payments - provides payment services.
The TON blockchain includes several chains:
1. Master chain. Contains information such as system parameters, working chain state,
hashes of all recent blocks, and the number of GRAM tokens issued.
2. Work chain. They connect chains of "shards". Each worker thread has a unique ID
and logic and can have its own virtual machine and address format. TON supports a
total of 232 work chains, and each work chain can contain up to 260 segment chains.
3. Broken chain. Ensure system expansion. You can share messages. Follow the chain
of command rules.
4. Chain of accounts. virtual chain.
5. Part of a fragment chain. They are a kind of register of incoming and outgoing
messages for a certain account.
The architecture used in TON provides a solution to two important problems - the large
size of the blockchain and the high complexity of making changes to the blockchain
architecture. The first problem is solved by special methods of data storage - the file can be
stored off-chain and store only the hash value of the file, or the smart contract containing
this data can be stored in the corresponding block. Information about conditions. data in
the block. The document is stored in the chain. The second problem is addressed by the
infinite sharding paradigm, which groups account chains into shard chains such that each
shard chain block contains a shard chain block. At the beginning of 2018, $1.7 billion was
raised for the development of the project as part of the ICO. Closed beta testing began in
April 2019. As of December 2021, one GRAM token was worth approximately $0.004.
In general, blockchain systems can be divided into two categories based on the
differences: public and private.
In a public system, access to the participating network is open, and anyone can create
new entries and have read access to existing entries. Such solutions are recommended for
cryptocurrencies, an example of such a system is Bitcoin,
In a private system, permissions are required to create new records or read existing
records. Applications for such systems include enterprise systems as well as manufacturing
and supply chains. Examples of such systems are Hyperledger, Hashgraph, R3 Corda, and
Quorum.
When determining the feasibility of using blockchain technology and determining the
type of blockchain system, the following basic data processing conditions must be taken into
account:
do you need data storage?
multiple users are required to record data;
lack of reliable data confirmed by third parties;
is anonymity required to determine the type of blockchain system required – public
or private;
whether a public profile check is required (to determine whether to use a public
exclusive system or a private exclusive system.
Some experts believe that the bills need serious changes.
In the United States, the IRS considers bitcoin a valuable asset and imposes a capital gains
tax on bitcoin transactions. Meanwhile, some US government agencies are trying to regulate
Bitcoin as a currency.
Blockchain technology has the potential to become Occam's Razor, the most efficient,
direct, and natural means of coordinating all human behavior in response to the natural
desire for balance.
2. Problem statement
It is assumed that personal data (PD) should be processed in the blockchain system at
least during the entire life cycle of the subject of personal data. During the entire process of
personal data processing, their safety, confidentiality, availability, integrity, and reliability
should be ensured.
Currently, the challenges of creating a blockchain system architecture that is free from
the main threats associated with blockchain systems include ensuring the reliability of the
processed data.
Therefore, the construction of a protected decentralized registry of personal data
(DRPD) boils down to solving the following tasks:
to determine the composition of a black hole, it must be processed in DRPD;
define the overall DRPD architecture;
determine the order of data storage;
determine mechanisms for reaching consensus including procedures for providing
rewards to users to ensure the operation of the DRPD and procedures for automatic
assessment of the risks of processing unreliable personal data;
select the hash function calculation method;
determine the general sequence of development of the DRPD.
It seems appropriate to use machine learning techniques to implement automated risk
assessment within consensus mechanisms. For example, it is recommended to identify four
characteristics that can be used to conduct a risk analysis:
the degree of formal connection between the consensus node and the confirmation
object;
mutual confirmation of participants in a PD network can indicate collusion and user
behavior that is distributed among multiple people and is therefore particularly
difficult to detect;
the amount of potential compensation to PD subjects;
reliability of consensus nodes and verification objects.
During the creation of the DRPD, the following issues must be resolved:
determine the purpose of PD processing and the corresponding PD components, and
their processing should be carried out in a decentralized system;
define the overall DRPD architecture;
determine the order of data storage;
develop a consensus mechanism to encourage user participation in ensuring the
functioning of the DRPD and conducting automated assessments of the risks of
entering and processing unreliable personal data in the DRPD;
determine the method of calculating the hash function;
to determine the general sequence of development of DRPD;
defines the method for calculating PD trade-offs when processing PD trade-offs in
DRPD.
Figure 2 shows the recommended approach to protecting personal data when using
DRPD.
Figure 2: The holistic approach of a decentralized registry of personal data to protecting
human resources.
3. Defining a distributed ledger architecture
Taking into account the purpose and composition of the PD, as well as the large volume
of data that must be processed in the DRPD, it is recommended. DRPD has several
independent private blockchains, one for each subject area:
the main chain blockchain is for data identification information (BDI);
blockchain of Work (BDE) for recording educational data;
the Jobchain (BDS) blockchain is for skills data;
blockchain Smart Asset Information (BDSA) workflow for asset data;
smart Contract Information Dedicated Blockchain Work Chain (BDSC) for Smart
Contract Information (PD Category - Other);
use second-generation blockchain technology as the basis of the main chain,
replacing transactions with blocks to reduce traffic and load on network node
computing resources;
using third-generation blockchain technology, a direct acyclic graph is used as the
basis for the BDE, BDS, BDSA, and BDSC work chains, as this approach will allow
references to specific files and other blocks to be included in the blockchain.
It is recommended that each blockchain contains a unique user ID and the following PD:
BDI - Information about identity cards and passport data: series and number, issue
and time of issue, sample personal signature, surname, first name, patronymic,
gender, date of birth, place of birth, place of residence, military information, family
information about status, information about children, information about a
previously issued passport;
BDE - information about education: name of the educational institution, teachers
and specialties, years of study, composition of subjects, and success rate;
BDS - information about professional skills: name of the place of work, unit and
position, work experience, job duties and components, key skills;
BDSA – Asset Intelligence: non-cash funds, stocks, mutual funds, bonds,
cryptocurrencies, real estate, vehicles;
BDSC - smart contract data: employment contracts, contracts for the provision of
various services, and contracts for the purchase and sale of goods.
Figure 3 shows a general block diagram.
Figure 3: Block structure of various chains.
The generalized DRPD hierarchy proposed by the authors is shown in Figure 4.
Figure 4: General hierarchy of decentralized registry of personal data.
It is recommended that DRPD nodes be divided into three types: consensus nodes, audit
nodes, and thin clients. Consensus nodes must participate in the formation of new blocks by
contributing PD to the block and distributing it throughout the network. The audit node
must contain a copy of the blockchain and ensure load distribution across the network,
jointly acting as a content delivery network (CDN), i.e. providing: data transfer between
light clients and consensus nodes; reducing the volume of transit used to prevent delays,
breakdowns and loss of communication in congested corridors and their intersections. Thin
clients are designed to be installed on platforms with lower performance characteristics,
including mobile platforms, and may contain only the necessary host information.
Therefore, people who use DRPD can be divided into two categories: users and
operators:
1. User:
Submit your PD to DRPD;
If necessary, obtain and provide third parties with access to your data;
If necessary, provide a personal device for storing data in an encrypted form.
2. Operator:
carry out technical control over the activities of DRPD;
Make sure the PD entered in the DRPD is correct, create a new block, and enter the
DRPD.
As a basis for DRPD, you can use ready-made solutions or develop new ones. The core of
the DRPD platform is proposed to be implemented in the Java 8 programming language
using a NoSQL database. Provides interaction between the RESTful API architecture and the
core of the platform. The Erachain platform has taken a similar approach to creating a
decentralized login code architecture specifically designed to handle personal data. Safe
software development practices are recommended when developing DRPD. In addition, the
methods of calculating the reliability of complex systems can be used in the design of DRPD.
This block should contain approximately 1.5 KB of identification information per person,
12 KB of education and skills information per person, and 1 KB of information per smart
asset and smart contract.
Based on the above, Table 1 guides the appropriate number of blocks that should be
generated initially during system creation and how often new blocks should be generated.
Table 1
A proposal for the number and frequency of creation of new blocks
The volume Number of Approximate volume of Average approximate
Name of the of one block blocks the blockchain frequency of creating
blockchain initially at the start Increase per new blocks
created year
300 KB 6 250 1.9 GB 0.1 GB 1 per day
BDI
2,400 KB 1 001 2.4 GB 2.6 GB 3 times a day
BDE
2,400 KB 2 815 6.8 GB 7.0 GB 8 times a day
BDS
200 KB 50 000 10.0 GB 7.6 GB 1 time every 10 minutes
BDSC
200 KB 50 000 10.0 GB 7.6 GB 1 time every 10 minutes
BDSA
Therefore, the current DRPD data volume will reach approximately 31.1 GB at system
launch and will grow to 24.9 GB each year.
Due to the specificity of the thematic fields, BDE does not publish new blocks every day
but mainly contains information about additional professional education received. But the
fall and spring will be the season when new quarters with information about secondary and
higher education will appear.
It is recommended to store large amounts of data on DRPD user personal data storage
media as audit nodes or consensus nodes. If biometric data needs to be processed, masking
compression methods based on weighted image structure models can be used. To ensure
the confidentiality of personal data, it is recommended to ensure that it is encrypted.
Current tasks also include the development and certification of decentralized systems using
blockchain technology to meet the requirements of the Cryptographical Information
Protection Facility (CIPF).
PD-distributed storage must be able to store large files. In addition, decentralized PD
storage requires a version control file system with persistent access capabilities that can
uniquely map unique files to their hash values to verify file integrity and the absence of
undeclared functions. An example of a system that can provide such functionality is the
InterPlanetary File System (IPFS) project. IPFS combines BitTorrent's peer-to-peer file-
sharing technology with the capabilities of Git, a decentralized version control system
created to manage software development, but can be used for any digital resource.
Transactions listed in a blockchain block may contain references to files stored outside the
network and methods of accessing them. In addition, IPFS is designed using direct acyclic
graph technology, is compatible with the technical architecture of cryptocurrency, and
rewards file-sharing nodes in the form of Filecoin coins. Therefore, IPFS can serve as a
technical solution for processing large volumes of data.
It is also recommended to include provisions for archiving unused blocks in the
blockchain. You can archive using the Internet Archive, the Wayback Machine, or similar
systems.
For DRPD, it is necessary to ensure the first level of security:
when used with DRPD, your computer may have software with undeclared features;
DRPD includes biometrics and other PD categories.
Creating new blocks doesn't have to be a time-consuming task. Considering the specifics
of the considered blockchain system, the Proof-of-Authority algorithm appears to be the
most appropriate, designed to ensure the operation of a private network and allow the
identification of privileged validators. Its functionality is proposed to be expanded with the
help of a program that automatically evaluates the reliability of data entered into the
blockchain system.
Figure 5 shows an overview of the PD record verification procedure proposed by the
authors in their notebooks.
Figure 5: Check the general register data input scheme.
Since DRPD requires user computing resources, which are used to store data and
perform calculations when new blocks are created, and users' confirmation of new blocks,
a user reward system must be established. Developed
One possible option is to implement a performance-based (PR) system.
The value of PR can increase based on success in education, career, contract fulfillment,
etc.
The reward for the creation of new blocks can be realized with the help of individual
accounting units of reward (AUR). At the same time, the opportunity to participate in the
creation of a new block and the probability of success should depend on the user's PR.
Reward methods and their definition require careful study and are beyond the scope of
this study. In Table 2, the authors provide general examples of possible reward values to
demonstrate the general principles of rewarding users. Fuzzy set theory can be used to
demonstrate the value of rewards. In the proposed example, users with a PR value of 6 can
become consensus nodes.
Thus, the unit of remuneration in accounting for mining activity is:
This applies to the BDSA network only;
After all, we are talking about a decentralized registry that randomly selects a PR value
of at least a given value and expresses its willingness to become a user of the consensus
node.
The probability of a consensus node winning depends on the user's PR value.
Table 2
Generalized example of possible reward values
Chain Basis for remuneration Reward amount by which the PR value
increases
All Registration 0.5
All Data storage from 10% to 100% chain 0.1–0.5
BDE Getting an education 0.5/1/2
BDE Advanced training/professional retraining 0.5/1
BDE Obtaining an academic degree 3/6
Registration of such information can satisfy the subject's desire for economic benefit,
high social status, etc. To ensure the reliability of entering registration information, it is
recommended to implement a mechanism for automatic assessment based on machine
learning methods of the risks of entering and processing unreliable personal data. As initial
data, it is recommended to determine the factors that create prerequisites for entering and
processing unreliable PDs in the DRPD. These factors can be:
increased probability of collusion between identified subjects and targets;
the possibility of substantial compensation;
the reliability of the confirmed object is low low PR value, reflecting low material
happiness and, a lack of necessary knowledge and skills.
For example, it is recommended to identify the four characteristics described in
subsection 2.2 and take into account that a risk analysis can be carried out:
the degree of formal connection between the consensus node and the confirmation
object;
degree of participation in networks of mutual recognition of personal data;
the amount of potential compensation to the PD subject;
reliability of consensus nodes and validation objects.
Therefore, in the considered paradigm, the risk of encountering an unreliable PD will be
represented by the risk of collusion between consensus nodes and validation objects. Each
presented feature can be represented by the coefficient , where n is the
number of the feature:
x1 - The degree of correlation between the consensus node and the confirmation
object;
x2 - Participation in the mutual confirmation of the PD network;
x3 - the potential amount of remuneration of the subject of personal data;
x4 is a consensus node and confirms the reliability of objects.
When confirming PD, it is recommended to use ANN as a mathematical tool for risk
assessment. Therefore, the input end of the neural network must have four input signals x1-
x4, and the construction of the ANN is reduced to solving the following problems:
determine the required type of ANN;
develop a method of assigning numerical values (x1-x4) to the input signals of ANNs
expressing analytical features;
determine the number of necessary ANN layers and the number of neurons in the
ANN layer;
selection of ANN training methods;
selection of the activation function;
select the NET output range to indicate the level of risk confirmed by the PD.
Figure 6 presents a summary diagram of the necessary ANN, proposed by the authors.
Figure 6: Generalized graph of ANN for determining the reliability of PD.
When learning ANNs, the importance of input values is determined by changing the
weight coefficients of neural connections. when building a neural network, the following is
recommended:
use the mathematical method of fuzzy set theory to assign values to input signals;
use the well-studied multi-layer fully connected perceptron as a feedback-free
neural network;
a neural network consists of three layers;
use the backpropagation algorithm as a learning method;
to minimize the RMS error of the neural network when training the neural network,
use the hyperbolic tangent as the activation function;
the range of initial values [-1;1] should be interpreted as follows: -1 - the minimum
risk of PD unreliability, and 1 - the maximum risk of PD unreliability.
Within the framework of the problem under consideration, the symbol xn is proposed to
be considered as:
The membership of 𝜇𝐴 (𝑢) to the eigenfunction of the set of values, A represents the
increased probability of reaching a given unreliable PD on the universal set U,
The value of the elements of the set U that belong to the set A is equal to 1, and the value
of the elements that do not belong to the set A is equal to 0:
1, 𝑖𝑓 𝑢 ∈ 𝐴 (1)
𝜇𝐴 (𝑢) = { .
0, 𝑖𝑓 𝑢 ∈ 𝐴
In this case, it is necessary to consider its own set for each membership function.
Examples of the four functions of object ownership are:
a function belonging to the set of values of the degree of connection between
consensus nodes and verified objects, under which the most favorable conditions of
collusion are created;
a membership function for a set of intermediate confirmation values that
demonstrate an increased probability of participation in a conspiracy;
determination of the membership function of the set of values of the reward object,
which provides the greatest incentive to participate in the conspiracy;
the overall value of the functionality and reliability of the verified objects included
in the group of consensus nodes creates minimal prerequisites for participation in
the conspiracy.
Figure 7 presents the Zade diagram 33, which shows the possible dependence of the
value of the characteristic membership function on the set of values of the degree of
connection between consensus nodes and verified objects, which creates the most favorable
conditions for a consensus conspiracy. The degree of contact between the node and the
confirmed object, at this time:
Ua - a set of values indicating the degree of connection between the consensus node and
the object being checked 𝑢𝑎 = [𝑢𝑎 , 𝑢𝑎 ∈ 𝑅: 0 ≤ 𝑢𝑎 ≤ 10];
Aa is a set of values of the degree of connection between the consensus node and the
object under test. With this value, the most favorable conditions for participation in the
conspiracy are created;
𝜇𝐴𝑎 (𝑢𝑎 ) is a characteristic function of this group value, which belongs to the degree of
connection between the consensus node and the verification object? With this characteristic
function, the most favorable conditions for participation in the conspiracy are created;
x(𝑢𝑎 ) – The degree to which the eigenvalue of the function belongs to the set of relation
values 𝜇𝐴𝑎 (𝑢𝑎 ) between consensus nodes and verified objects, among which the most
favorable entry condition belongs to the collision.
Figure 7: Dependence on the degree of connection between the verification object and the
consensus node and favorable conditions for collusion.
In the given example, it is assumed that 0 corresponds to different degrees of
connectivity, 6 along the abscissa axis.
After PR, as the degree of contact between the consensus node and the verification object
increases and the most favorable collusion conditions appear, the membership possibilities
continue to increase.
For a more convenient interpretation of data when forming training samples and, if
necessary, their normalization, it seems recommended to reduce the obtained results to a
general representation of fuzzy subsets:
10 0 10 (2)
𝐴𝛼 = ∑ 𝜇𝐴𝑎 (𝑢𝑎 )⁄𝑢𝑎 = ∑ 0,00⁄𝑢𝑎 + ⋯ + ∑ 1,00⁄𝑢𝑎 .
𝑢𝑎 =0 𝑢𝑎 =0 𝑢𝑎 =10
When forming training samples, it seems recommended to determine values that can
negatively affect the learning process of neural networks - these values do not allow us to
draw clear conclusions about anomalies in user behavior. In the theory of fuzzy sets, this
value is determined by the transition point. For the membership function µ A, such a point
is a = 5.
Figure 8 shows the Zade diagram, which shows the possible dependence of the value of
the attribution function in the set of probability of collusion of the PD subject on the degree
of mutual confirmation of participation:
Ub - a set of intermediate confirmation values;
Ab - a set of confirmed intermediate values showing an increased probability of
participation in a conspiracy;
𝜇𝐴𝑏 (𝑢𝑏 ) is a characteristic function of the degree of belonging to a set of intermediate
confirmation values, which demonstrates an increased probability of participation in a
conspiracy;
x(ub) is the value of the characteristic membership function µ Ab (ub) for the set of
intermediate values of the confirmation, which represents the increased probability of
participation in the conspiracy.
Figure 8: The relationship between the level of participation and the probability of mutual
confirmation of the conspiracy IoT.
In our research, the horizontal axis shows the ub values corresponding to the
intermediate confirmation numbers: 0, 3, 5, 10, 25, 100, 1000.
Since PR, as the number of mutual confirmations increases (as the value of ub increases),
the probability of collusion decreases (the value of x(ub) decreases) and the membership
function also decreases.
The general form of recording fuzzy subsets will have the following form:
1000 0 1000 (3)
𝐴𝑏 = ∑ 𝜇𝑏𝐴𝑏 (𝑢𝑏 )⁄𝑢𝑏 = ∑ 0,9⁄𝑢𝑏 + ⋯ + ∑ 0,1⁄𝑢𝑏 .
𝑢𝑎 =0 𝑢𝑏 =0 𝑢𝑏 =1000
The transition point of the membership function x(ub) = 0.5 is equal to ub = 25.
Figure 9 shows a Zade plot illustrating the possible dependence of the eigenvalues of the
membership functions in the set of conspiracy motivation values on the potential reward
size of the confirmed object, where:
Uc – identifies a set of potential object reward values expressed as PR values;
Ac is the set of reward values that provide the maximum incentive to collude for
confirmed objects;
𝜇𝐴𝑐 (𝑢𝑐 ) – Determine the characteristics of the membership function that provides the
maximum incentive for collusion within a set of target reward values;
x(uc) is the value of the characteristic membership function, which determines the size
of the object's reward and provides the greatest incentive for collusion.
The examples below assume the following:
the higher the potential reward, the higher the incentive to collude;
the following key salary values can be distinguished, reflecting certain
achievements: 0.1;
the goal is to demonstrate with concrete numerical examples the general principles
of membership functions that shape the characteristics of neural networks and
future input values.
Figure 9: The relationship between the size of the reward and the motivation to participate
in the conspiracy.
Due to PR, as the reward increases (as uc increases), the incentive to collude increases as
x(uc) increases, and the membership function also increases.
The general form of recording fuzzy subsets will have the following form:
9 0 9,0 (4)
𝐴𝑐 = ∑ 𝜇𝐴𝑐 (𝑢𝑐 )⁄𝑢𝑐 = ∑ 0,9⁄𝑢𝑐 + ⋯ + ∑ 1,00⁄𝑢𝑐 .
𝑢𝑐 =0 𝑢𝑐 =0 𝑢𝑐 =9,0
The transition point of the membership function x(uc) = 0.5 is uc = 1.
Figure 10 shows a Zade plot showing the possible dependence of the values of the
characteristic membership functions in the set of conspiracy motivation values on the
reliability of consensus nodes and confirmation objects, where:
Ud is a consensus node, which is a set of values confirming the overall reliability of
the object represented by the PR value;
an announcement is a set of trust points that creates the minimum prerequisites for
participating in a conspiracy;
𝜇𝐴𝑑 (𝑢𝑑 ) is a characteristic function belonging to a set of values that creates minimum
prerequisites for participation in a conspiracy;
x(ud) –A functionally important characteristic of a set of reliability values, which
creates minimal prerequisites for participation in a conspiracy.
This research assumes the following:
the higher the overall authority and verification goal of the consensus node, the
lower the incentive for collusion;
the PR value of the consensus node is 8, which is 100% reserved after registering
with DRPD;
fields of scientific degree and availability of candidates of sciences.
As an example, below are the values of control points PR and ud of matched nodes:
confirmation that the goal has been achieved: 13 have successfully repaid a loan of
10 million rubles;
consensus points reached: 46.5, 52.5, 70.5, 73.1, 74.
The goal is to demonstrate with concrete numerical examples the general principles of
membership functions that shape the characteristics of neural networks and future input
values.
As PR increases along with the trustworthiness of consensus nodes and validators as ud
increases, the probability of collusion decreases as x(ud) increases and the membership
function increases.
Figure 10: Dependence on the reliability of the verification object and the consensus node
and the probability of collusion.
Therefore, the general form of recording fuzzy subsets will have the following form:
74 9,0 74 (5)
𝐴𝑑 = ∑ 𝜇𝐴𝑑 (𝑢𝑑 )⁄𝑢𝑑 = ∑ 0,10⁄𝑢𝑐 + ⋯ + ∑ 1,00⁄𝑢𝑑 .
𝑢𝑑 =0 𝑢𝑑 =0 𝑢𝑑 =74
The transition point of the membership function x(ud) = 0.5.
Any continuous function of m variables on the unit interval [0 1] can be expressed as the
sum of a finite number of one-dimensional functions:
2𝑚+1 𝑚 (6)
𝑓(𝑥1 , 𝑥2 , … , 𝑥𝑛 ) = ∑ 𝑔 (∑ 𝜆𝑖 𝜑𝑝 (𝑥𝑖 )),
𝑝=1 𝑖=1
where the functions g and 𝜑𝑝 are one-dimensional and continuous, i = const for all i. It
follows that any continuous function can be approximated by a three-layer neural network
with m input neurons, 2m + 1 hidden neuron, and 1 output neuron. This result is extended
to multilayer networks using the backpropagation algorithm.
Thus, the final neural network contains three layers. As the number of neurons in the
hidden layer increases, on the one hand, the accuracy of the artificial neural network
increases, but on the other hand, if the scale of the hidden layer is too large, it will cause the
network to be overloaded and result in the network being too large. Accuracy is also
degraded. The generalizing ability of ANNs. Therefore, the number of neurons in the
network should be minimized.
According to the proposal to determine the number of neurons in a neural network
based on the number of training pairs, it is recommended to use the following formula:
2(𝑚1 + 𝑚2 + 𝑚3 ) < 𝐿 < 10(𝑚1 + 𝑚2 + 𝑚3 ), (7)
where m1 is the number of input layer neurons, m2 is the number of hidden layer neurons,
m3 is the number of output layer neurons, and L is the number of training pairs.
Taking into account the degree of use of neurons in the layer can be expressed as:
6𝑚1 + 4 < 𝐿 < 30𝑚1 + 20, (8)
when training a neural network, use samples drawn from a distribution close to the true
one. The distribution used has a ratio of invalid to valid data of approximately 1:99.
So, the neural network contains 1001 neurons, 333 of which are in the input layer, 667
in the hidden layer, and 1 in the output layer.
The initial configuration is a three-layer fully connected homogeneous feedback-free
perceptron with four inputs, a thousand neurons, and a hyperbolic tangent as the activation
function. The first layer contains 333 neurons, 667 hidden layers, and 1 output layer.
When forming the training set, validation set, and test samples:
1. Use the following principles:
principles of sequential experiments;
standardization of factors;
validation and testing samples should be drawn from the same data distribution -
approximately 1% unreliable PDs and 99% reliable PD`s.
2. Make the following assumptions:
the frequency of errors during the classification of training, validation, and test
samples (errors in marked examples before neural network training) is 1%;
due to the large size of the training set, it seems more appropriate to split the test
set into eye sample PR and black box selection PR;
the execution time of the algorithm will never exceed the maximum allowable value.
According to the recommendations, the number of training pairs L is determined by the
following formula:
2(𝑚1 + 𝑚2 + 𝑚3 ) < 𝐿 < 10(𝑚1 + 𝑚2 + 𝑚3 ), (9)
where m1, m2, m3 are the number of neurons in the layer. Therefore, the number of
training pairs L should take values in the range [2002;10010].
Use the 10,000 training pairs as training samples to express your own set of features and
create images that will be fed to the neural network input.
Since the range of values of the hyperbolic tangent is limited, the training set is rescaled
to the appropriate range of values.
The neural network is trained on the training set until a given mean squared error is
reached.
Validation and test samples include 3000 pairs. To enable rapid manual estimation of
classification error and to avoid overfitting, the samples were split into eyeball samples and
black box samples consisting of 500 and 2500 pairs, respectively. Assuming a mislabeled
sample rate of 1%, a sample of 500 pairs of eyeballs will contain approximately 5 mislabeled
samples. It turns out that the number of unclassified examples is insufficient for error
analysis.
However, to avoid overtraining the network, it was decided not to include all 3000 test
samples in the eye samples, but to prioritize the selected samples into a black box, which
will generate up to 5 new eyes 500 per sample.
If there are large errors, it is concluded that the neural network is underequipped and
additional training is performed. If the deviation is small, but the variation is large, it can be
concluded that the neural network is overloaded. The neural network is trained until the
bias and distribution reach a certain target value.
To determine the quality of neural networks, it is recommended to use multi-parameter
PR metrics, including:
Satisfaction indicators:
the average correspondence between accuracy and completeness is not less than
0.6;
dispersion - no more than 0.5%;
the value of the PR shift of the optimization indicator should not exceed 1%.
During the training process, the possible output characteristics of training and testing
include two categories: valid data input and invalid data input. At the initial stage, the class
consists of the following pairs:
Training set - 4960 pairs of reliable data and 40 pairs of unreliable data;
There are 3970 pairs of valid data and 30 pairs of invalid data in the control sample and
the test sample.
The results displayed by the neural network at the initial stage of training are shown in
Table 3:
1. The reliability of the neural network is equal to:
12 + 1818 (10)
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = ~0,61,
12 + 1152 + 18 + 1818
2. The accuracy coefficient of the neural network is equal to:
12 (11)
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = ~0,01,
12 + 1152
3. The integrity of the neural network is equal to:
12 (12)
𝑅𝑒𝑐𝑎𝑙𝑙 = = 0,4,
12 + 18
4. F1-measure of the neural network:
0,01 × 0,4 (13)
𝐹1𝑠𝑐𝑜𝑟𝑒 = 2 ∼ 0,02,
0,01 + 0,4
5. Deviation of the training sample - 34%, deviation of the test sample - 39%.
6. The spread is 5%.
Table 3
Results of the initial stage of neural network training
Parameter Meaning
Learning results on the training set
True Positive (TP) 16
False Positive (FP) 1336
False Negative (FN) 24
True Negative (TN) 2624
Results of testing on the validation set
True Positive (TP) 12
False Positive (FP) 1152
False Negative (FN) 18
True Negative (TN) 1818
Since a 34% bias in the neural network results was found in the early stages of PR
training, it was decided to increase the size of the neural network by adding neurons to the
input layer, according to the proposal. that's why:
the size of the neural network increased from 1001 neurons to 1004 neurons: 334
neurons in the input layer, 669 neurons in the hidden layer, and 1 neuron in the
output layer;
the number of training, validation, and test sample pairs does not change, as the
number of training pairs falls into a new range [2008;10040].
The results after completing the training of the extended neural network are shown in
Table 4. So, after completing the training on the test sample:
1. The reliability of the neural network is equal to:
25 + 2953 (14)
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = ~0,99,
25 + 17 + 5 + 2953
2. The accuracy coefficient of the neural network is equal to:
25 (15)
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = ~0,60,
25 + 17
3. The integrity of the neural network is equal to:
25 (16)
𝑅𝑒𝑐𝑎𝑙𝑙 = ∼ 0,83,
25 + 5
4. F1-measure of the neural network:
0,60 × 0,83 (17)
𝐹1𝑠𝑐𝑜𝑟𝑒 = 2 ∼ 0,69,
0,60 + 0,83
Table 4
Results of the neural network after training
Parameter Meaning
Learning results on the training set
True Positive (TP) 35
False Positive (FP) 19
False Negative (FN) 5
True Negative (TN) 3941
Results of testing on the validation set
True Positive (TP) 25
False Positive (FP) 17
False Negative (FN) 5
True Negative (TN) 2953
Results of testing on a test sample
True Positive (TP) 23
False Positive (FP) 21
False Negative (FN) 7
True Negative (TN) 2949
The deviation of the training set is 0.6%, and the deviation of the test set is about 0.7%.
The difference (deviation between training and test samples) is about 0.13%. On the test
sample:
1. The reliability of the neural network is equal to:
23 + 2949 (18)
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = ~0,99,
23 + 21 + 7 + 2949
2. The accuracy coefficient of the neural network is equal to:
23 (19)
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = ~0,52,
23 + 21
3. The integrity of the neural network is equal to:
23 (20)
𝑅𝑒𝑐𝑎𝑙𝑙 = ∼ 0,77,
23 + 7
4. F1-measure of the neural network:
0,52 × 0,77 (21)
𝐹1𝑠𝑐𝑜𝑟𝑒 = 2 ∼ 0,62.
0,52 + 0,77
5. The deviation is about 0.93%.
6. The deviation between the control sample and the test sample is 0.2%.
The final configuration is a three-layer homogeneous open-loop perceptron with 4
inputs, 1004 neurons, and a hyperbolic tangent as the activation function. The first layer
contains 334 neurons, the hidden layer - 669, the output layer - 1.
4. Discussing
The topic of encryption is beyond the scope of this study. But since PR blockchain systems
are by definition based on the use of cryptographic methods, it seems appropriate to
provide general advice on the choice of methods for calculating hash functions.
Since a private blockchain is chosen as the basis for the blockchain system, methods
based on symmetric encryption rather than asymmetric encryption as in public blockchain
systems should be used as a method of cryptographic protection for blocks and
transactions.
Given the urgency of the task of creating a post-quantum cryptosystem, it is necessary to
foresee the possibility of making changes to the process of calculating hash functions in the
developed scratchpad architecture.
DRPD access and PD storage are expected to use encryption to protect information. Even
if the key is broken, the confidentiality of the protected data can be ensured by ensuring the
confidentiality of the carrier signal energy and the confidentiality of these structures.
Signals reliability of detection, complexity of the signal structure. However, this trade-off
seems reasonable given the need to consider data transmitted over the Encrypted PD
protocol.
Data paths in DRPD can be divided into two categories: non-overlapping paths and
overlapping paths. DRPD is an on-call system.
The proposed method of calculating the probability of data leakage assumes the
following:
the sending node and the receiving party are protected, that is, the possibility of
being attacked by hackers is zero;
if one segment of a path (a node, a data link, or a combination thereof) is
compromised, all data traveling along that segment will also be compromised.
Suppose that the following initial information is known:
pji is the probability of damage to the j-th segment of the i-th path;
Qi is the number of damaged segments on the ith path.
Then the probability that the i-th trajectory consisting of airborne debris will be
destroyed can be calculated using the following formula:
𝑄𝑖 (22)
𝑝𝑖 = (1 − 𝑝𝑖1 )(1 − 𝑝𝑖2 ) … (1 − 𝑝𝑖𝑀𝑖 ) = 1 − ∏(1 − 𝑝𝑖𝑗 ),
𝑗=1
when the data is divided into N parts (N, N) according to the Shamir scheme and transmitted
through Q paths, the probability of data leakage is determined by the following expression:
𝑄 (23)
𝑃𝑚𝑠𝑔 = ∏ 𝑝𝑖 ,
𝑖=1
where Q is the number of non-intersecting paths used to route data elements – the
probability that the i-th path segment is compromised.
In the case of using intersecting routes with a series of connecting line segments and
parallel structures, the probability of data theft is calculated according to the following
formula:
̃
𝑁 (24)
𝑃𝑚𝑠𝑔 = 1 − ∏(1 − 𝑝̃𝑗 ),
𝑗=1
where 𝑁̃ is the total number of sequence segments in the series-parallel structure of the
considered intersecting paths; 𝑝̃𝑗 is the probability of destruction of the j-th segment.
To demonstrate the general principle of the proposed method for calculating the
probability of data leakage, Figure 11 shows a rather simplified structure of the DRPD,
which consists of two connected paragraphs:
The first segment includes a parallel connection of the communication channel 1→3
and the sequence of channels 1→2 and 2→3;
The second segment is represented by the communication channel 3→4.
Figure 11: Examples of serial and parallel connection of components
The probability of leakage of the first and second points is determined by the probability
of leakage of the communication channel that they form:
𝑝𝑖 = 1 − (1 − 𝑝
̃)(1
1 −𝑝
̃),
2 (25)
The probability of data leakage is calculated as follows:
̃1 = (1 − (1 − 𝑝1 )(1 − 𝑝2 ))𝑝3 , 𝑝
𝑝 ̃2 = 𝑝4 (26)
Among them, ə is the total number of parallel segments; ə is the probability of
destruction of the j-th segment.
The most common scenario for decentralized systems based on blockchain technology
seems to be the use of intersecting paths with complex structures that allow network
segments to be connected in series and parallel. For clarity, Figure 12 shows a general
example of such a structure consisting of seven paragraphs:
Fragments 1, 2, and 3 are connected in series, forming fragment 4;
Segment 5 and Segment 6 are connected in series to form Segment 7;
Fragments 4 and 7 are connected in parallel.
Figure 12: Example of a combination of elements
The probability of data leakage will be determined by the following formula:
𝑃𝑚𝑠𝑔 = 𝑝
̃𝑝
4 ̃,
7 (26)
The probability of damage to segments 4 and 7 is represented by the probability of
damage to the corresponding communication line:
̃4 = (1 − (1 − 𝑝
𝑝 ̃)(1
1 −𝑝̃)(1
2 −𝑝 ̃));
3 (27)
̃7 = 1 − (1 − 𝑝
𝑝 ̃)(1
5 −𝑝̃)6 = 1 − (1 − 𝑝7 )(1 − 𝑝8 ).
A method of ensuring the reliability of personal data processed in blockchain systems is
proposed. The approach includes recommendations for creating a common architecture for
decentralized ledgers, an agreed PD for data storage, methods for reaching consensus, a
common agreed PD for system implementation and development, and calculating the
probability of data theft.
5. Conclusion
The category of data reliability methods is expanding due to the use of artificial neural
networks to identify unreliable personal data when entered into blockchain systems.
The reliability of personal data processing in blockchain systems can be ensured using
the proposed method:
WIPO single-level cloud platform or at the country level;
as part of ensuring compliance with the requirements for monitoring incorrect user
actions when entering personal data.
This method differs from known methods by the unique architecture of the information
system of personal data. This method differs from known methods in that it uses a
conceptually new consensus approach that involves an automated assessment of the risks
of implementing unreliable material handling. The theory of artificial neural networks and
the theory of fuzzy sets.
Thus, the task proposed in the article has been solved to develop a method for ensuring the
reliability of personal data processed in the blockchain system, and when the data enters
the blockchain system, its reliability will be automatically evaluated.
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