=Paper=
{{Paper
|id=Vol-2563/aics_39
|storemode=property
|title=Enabling Multi-Party Consensual Data Exchange Through Blockchain
|pdfUrl=https://ceur-ws.org/Vol-2563/aics_39.pdf
|volume=Vol-2563
|authors=Jack Hickey,Ruairi O'Reilly
|dblpUrl=https://dblp.org/rec/conf/aics/HickeyO19
}}
==Enabling Multi-Party Consensual Data Exchange Through Blockchain==
https://ceur-ws.org/Vol-2563/aics_39.pdf
Enabling Multi-Party Consensual Data
Exchange Through Blockchain
Jack Hickey1 and Ruairı́ O’Reilly2
1
Jaguar Land Rover,
jhickey3@jaguarlandrover.com
2
Cork Institute of Technology, Ireland,
ruairi.oreilly@cit.ie,
www.cs.cit.ie
Abstract. The quantity and quality of data available to an organisa-
tion plays an increasingly important role in its operation. This data can
relate to a variety of subjects, from the internal logistics to consumer
sentiment towards a product in a specific market. This data provides
increasingly optimal behaviour derived from its analysis, e.g. improved
decision making. As more advanced, application-specific, machine learn-
ing models are developed, the organisation with the largest share of data
will gain an advantage over its competitors.
It is postulated that smaller entities with minority shares in data within
a domain possess only a fragmented view of a market. This fragmented
view puts the smaller entity at a disadvantage and enables larger entities
to reap unfair, competitive advantages. This unequal dynamic should be
rectified. To that end, in this work, a model is proposed that enables the
consensual sharing of data between multiple parties using blockchain.
Keywords: Blockchain, Data Analytics, Distributed Systems, Federated Learning
1 Introduction
Growing quantities of data enable the creation of increasingly accurate and re-
liable models of consumer behaviour, sentiment, and risk-analysis [4]. Notably,
companies with the largest share of market data have an advantage over their
competitors within their domain. The insights the smaller entities have present
a fragmented view of the market. This fragmented view damages their ability
to monitor trends and consumer sentiment effectively, ultimately resulting in a
less competitive market space.
When a dominant player emerges, they will utilise their data advantage for
analytics to garner insights into their business revealing information, such as
consumer behaviour and price points, as well as trends within the industry it-
self. This domination could potentially hurt the organic growth of the industry.
Developing a fuller picture of the market would give smaller entities the abil-
ity to compete against dominant players, fostering a more competitive market
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
place. Sharing fragmented data amongst entities within the same domain could
generate a more complete view of the market.
Sharing data between competing entities is challenging for numerous reasons.
Forming an agreement between multiple organisations is difficult, as organisa-
tions are reluctant to share their data as they fear it provides a competitive
advantage [9]. The difficulty of reaching an agreement is further complicated
due to privacy concerns [3] and legal restrictions [5] surrounding the security of
the data involved.
The benefits of multi-party data sharing have the potential to increase the
value that can be derived from data analytics for these companies. Fully exe-
cuting an agreement that addresses data security, privacy concerns, and legal
considerations is an addressable challenge.
Blockchain is a technology built around consensus. Consensus in blockchain
allows a decentralised network of members who do not have to trust each other to
form an agreement. Consensus is achieved through immutable smart contracts,
which is code that acts as the form of trust for these members.
This paper proposes the use of blockchain to allow competing entities to
reach a consensus of sharing fragmented data to contend against the dominant
players. Ultimately, it seeks to present a model that both facilitates competition
and enables the collaborative sharing of data such that optimal market insights
can be garnered. The challenge in providing such a solution can be summed up
in three points:
1. Consensus, creating a consensus model where all parties involved can agree
that the data is shared equitably.
2. Data privacy, data is appropriately handled such that it is not personally
identifiable.
3. Legal, ensuring the system is lawful with regards to data sharing and GDPR.
This paper focuses on the technical challenges in provisioning these three
characteristics through the use of digital agreements via blockchain, ultimately
providing a means for competing parties, who do not trust each other, to ex-
change data. Furthermore, the potentials of such a system to improve the ability
of smaller entities to compete against dominant players within a data market
are explored.
This paper postulates that the consensus of blockchain can be utilised to
provide a means for multiple competing parties who do not trust each other to
exchange data. This hypothesis is examined in the context of allowing smaller
entities to share their fragmented market view in order to garner a complete
perspective and compete against the dominant players within their respective
domains by exchanging data.
Future developments of the model propose its usefulness within the areas of
data analytics and machine learning. A use case examined is the application of
the model to federated learning in which the design holds a particular affinity.
The remainder of this paper is organised as follows: Section 2 reviews work
previously carried out that addresses similar. Section 3 presents an overview
of the model and introduces the approach taken to achieve consensus between
parties. Section 4 details the proof of concept implemented and explores how
a fully working model could be realised. Section 5 presents the conclusion and
findings of this paper and discusses some planned future work.
2 Related Work
The value of data is increasingly accepted and is what enables companies to com-
pete and stand out within their respective markets. In markets that are heavily
data-dependent, a few key players have dominated. The hypothesis presented
is that smaller entities often only have a fragmented view of the market place,
making it difficult to compete against a dominant player.
This section looks at how blockchain can be utilised to bring about consensus
to share data between these smaller entities who are competing with each other.
Application of blockchain in this context would allow smaller entities to garner
a more complete view of the market and increase their competitiveness against
a dominant player.
The application of such a system is also examined for the potential for feder-
ated learning to improve machine learning models. This is considered a potential
use case of the consensus model. A working assumption of the paper is that those
with a minority share of the data domain have a fragmented view of their market.
2.1 Blockchain
Blockchain in its basic form is an immutable ledger which allows transactions to
take place in a decentralised manner [10]. Blockchains use a consensus algorithm
which dictates what constitutes a legitimate write to the network and is agreed
upon by all parties involved. Three main types of blockchain have emerged:
public blockchain, consortium blockchain, and private blockchain [2].
Public blockchain, as implied by the name, allows anyone to view records
and take part in the consensus process. Private blockchain only grants write
permissions to one centralised organisation. Consortium blockchain consists
of only a pre-selected group of nodes able to participate in the consensus algo-
rithm, where the right to read may be public or private.
The application proposed is open to organisations to run models on data;
there must be a mechanism in place to ensure data privacy by restricting ac-
cess to the data itself as well as to the entire network. Public blockchains are
automatically exempt for this reason. Similarly, the network does not want the
control to be in a single, centralised authority; thus, private blockchains also
do not meet the requirements. A consortium blockchain would allow for each
organisation within the network to have an equal say within the consensus.
Blockchain is a technology that will disrupt many fields, and its uses for
sharing data has already been explored [7]. The use of blockchain’s consensus, as
opposed to a centralised authority, enables decentralised organisations to work
together, using the rules and consensus algorithm of the blockchain network
to dictate an agreement and remove any reliance on a centralised authority.
Applying blockchain consensus to our model would allow multiple parties to
collaborate in sharing their data while using the transparent rules to regulate
its sharing.
Blockchain has also been utilised for preserving data privacy [11] by using
blockchain as a transparent data access control manager that relinquishes the
need for human intervention when providing data access, relying on the power
of the consensus algorithm. The same idea of a data access control manager
by abstracting data access through the blockchain is applied in this work. The
blockchain network will have direct access to all data, while the parties will only
be able to access the sanitised data that the blockchain permits.
Blockchains often already use a certain kind of consensus algorithm with
little flexibility in how consensus is achieved. The model requires a consensus
algorithm that can accommodate the various needs of different markets. Flexi-
bility is required so that rules can be put in place that allows each party to reach
consensus.
This is why we propose using Hyperledger Fabric (HF). HF is an open-source
and permissioned blockchain with a modular consensus algorithm [1].
2.2 Consensus
Blockchain technologies are typically associated with a fixed consensus protocol
such as proof of work (POW) or proof of stake (POS) [10]. Consensus is agree-
ing about the legitimacy and order of transactions that occur on the blockchain
network. A minority of blockchain technologies utilise a modular consensus pro-
tocol that gives the developer a say in what constitutes an accepted block in the
network.
HF is unique amongst blockchain technologies, in that HF’s consensus cov-
ers the entire process from proposal and endorsement to ordering, validation
and commitment [1]. The consensus protocol provided with HF is exceptionally
flexible, leaving the architect to decide what constitutes an agreement on the
network.
Consensus for this project is an agreement between the entities exchanging
data on the network. The consensus protocol outlines the rules that the entities
involved will agree upon as to what is allowed on the network in terms of writes,
queries, and data access as well as anything specific to each industry request. to
The blockchain network enforces this. Achieving consensus between the compet-
ing entities to allow them to share data is automated through the design of the
blockchain consensus algorithm. The algorithm is simply code that enforces the
contract between these entities.
Using blockchain allows these parties to never directly interact with each
other and not explicitly trust each other. The transparency of the consensus
protocol and the ability to audit the chain and its transactions allows these
parties to trust the model, as opposed to each other.
2.3 Federated learning
Federated learning is a potential future use-case that this model could be applied
to and so is worth discussing. Federated learning is an approach that enables
collaborative training of machine learning models without sharing raw data [6].
Federated learning trains the model on edge devices using the local data and
summarises the changes as an update to the centralised model that the devices
contribute to. Edge devices, by their definition, could be considered minority
data stakeholders. This data privacy-oriented approach to machine learning has
been by adopted by organisations in much the same way, known as multi-party
federated learning.
Federated learning comes with its own set of challenges, most notably the
high communication cost of a massively distributed system and security [8]. As
the number of participants involved grows, the more challenging management of
the system becomes. The different hardware and network communication vari-
ants make fault tolerance and stragglers more prevalent than in typical data
centre environments. In a system where competitors intend to share data, data
ownership is a requirement, as is the capacity to verify and validate that owner-
ship.
The blockchain model removes the need for a centralised model, while also
providing the basis for training the models without the need for exchanging data
with the other participants. The blockchain model also provides easily verified
data ownership and the coordination of results and trained models through the
ordering of blocks.
3 Methodology & Design
This paper explores how to form an agreement to share data between competing
entities. Consensus enables parties to agree to how they will share data-this is
the true value of using blockchain.
The backbone of blockchain is its consensus algorithm. Each blockchain of-
fers a method for reaching consensus that a block is valid. This method is a
combination of algorithm and smart contracts. Using smart contracts a flexible
means of reaching consensus can be provided for the sharing of data, while also
providing transparency into how this is achieved.
We assume that entities will only participate if there is value, security, and
most importantly, consensus.
1. Value: The contribution of other entities matches the value of a data contri-
bution. The output of this contribution must also be equally valuable to the
other entities.
2. Security: The guarantee that their data is secure, as well as the security of
upholding the contract between parties.
3. Consensus: All parties must agree to the consensus algorithm that the blockchain
network enforces. This consensus upholds the previous two points and is the
underpinning technology that supports this system.
The goal of the system is to achieve consensus to share data between compet-
ing parties. The blockchain network is the backbone of the system for providing
this consensus.
Figure 1 outlines the system. Member refers to any participating entity of
the network who shares data within their respective domains. They can perform
several actions relating to the chain and management of their data.
Running queries is abstracted through a service, as depicted in Figure 1. The
service acts to ensure the consensus is preserved and runs on the blockchain
network. The entity runs its own set of data related queries through the service.
The service queries the blockchain network by first checking the contract is
being upheld. It then runs the queries which are returned to the service. The
results of the queries are compiled within the service, and personally identifiable
information (PII) is removed. The final result of all these queries is returned to
the member. Finally, the result writes a log relating to the type of data accessed
so that the other members of the system can audit this.
The blockchain network is the core of the system that enables the parties
to interact with the shared data. Abstraction of the data helps to uphold data
privacy and trust.
Fig. 1. An overview of member interaction with the blockchain model
3.1 The Consensus Protocol
In HF, consensus is the entire transactional flow, which serves to generate an
agreement on the order and to confirm the correctness of the set of transactions
constituting a block.
In the context of the proposed model, consensus describes the process by
which the members of the network reach an agreement to share data. This con-
sensus is backed up by the chaincode, which is the term used by HF to describe
smart contracts and the permissions of the HF network.
There are several principles of the system that should be upheld by the
network to ensure consensus: Data Integrity, Data Equality, Model Integrity
– Data Integrity is a means that ensures each organisations data cannot be
tampered with or exposed.
– Data Equality addresses concerns that some organisations may monopolise
data ownership. Providing mechanisms that discourage inequity and enable
greater sharing of data amongst minority entities will assist in the prevention
of data monopolisation.
– Model integrity enables each organisation to keep the models and their
respective results private.
Smart contracts are simply code that enforces the rules of the network. In
order for a business to perform transactions of data on the network, the con-
tracts must be defined. These smart contracts in HF cover terminology, data,
rules, concepts definitions, and processes. Smart contracts govern all interactions
between the parties involved. This is the most critical part of the HF network for
reaching consensus on how data is exchanged on the network. Smart contracts
are how consensus is achieved.
HF has two methods for keeping data private within a transaction: channels
and private data collections. Channels work as methods of communicating be-
tween organisations that enable the data of a transaction to be revealed only to
those involved in the channel. Unlike channels, private data collections enable
portions of the data to remain private between organisations.
HF has the concept of roles which includes peers, orderers, client applications,
administrators and more. HF uses this to determine permissions. The permis-
sions are incredibly flexible and can be related to the member’s organisation,
unit, role or even specific identity. The use of applying rules to an organisa-
tion can enable system equality to be upheld by ensuring that each organisation
abides by the same general rules. The flexibility of the permissions then enables
each organisation to define the permissions of roles further so that they can
have control over the actions its members can take. This is all done through the
membership service in HF.
Policies are another part of HF that will be utilised to reach consensus.
Policies contain the list of organisations that have access to a given resource and
also specifies how many organisations need to agree on a proposal to update
a resource, such as a channel or smart contract. With policies, changes to the
network between organisations are agreed upon and implemented without the
need for third-party intervention.
4 Implementation
A proof of concept (POC) was developed to investigate the feasibility of the
proposed solution. The POC used Hyperledger Composer, a tool for developing
on Hyperledger Fabric that has now been designated for developing POCs.
The POC developed is a data exchange network with permissions in place
for controlling data access. The POC enables data to be stored privately on the
network, only accessible to those with permission. Functionality was developed
that would query the data on the network only returning that which an entity
had been granted permission to access. The queries, however, did not sanitise
the data. Each entity can exchange data on this network in order to increase
the data they can access. This simple data exchange shows the feasibility of
using smart contracts and permissions on HF to provide a means of reaching
consensus.
Continuing from this demonstration of data exchange, the intent is to show
the added value of the increase in data. This value is realised by demonstrating
that through the combination of fragmented data, one can increase the accuracy
of machine learning techniques and data analytics.
Beyond this, we would then develop further the permissions to allow direct
data access to the owners of the data. The service would be added to sanitise
and control data access and uphold the full rules of the contract discussed in
section 3.
5 Conclusions
Achieving consensus of data exchange between competing parties is challenging,
though it is a challenge worth addressing, as the value of a more complete market
view allows parties with minority stakes of data to contend against dominant
players.
This work postulates that the permissioned blockchain technology Hyper-
ledger Fabric will enable competing parties to achieve consensus for data ex-
change. Introducing blockchain allows for smart contracts to act as an immutable
means of consensus for parties in determining data exchange. The privacy-
preserving features of Hyperledger Fabric could enhance the value of a system,
ensuring the security of each parties data, as well as potentially being more
legally compliant.
Blockchain and its immutability can provide a viable means for competing
parties to achieve consensus for data exchange. This will enable parties with
minority stakes in market data to garner better insights into their respective
markets and compete against dominant players.
The consumer has as much to gain as a business. Providing a more complete
view of the market to smaller parties allows for greater competition and creativ-
ity within a market. The enhanced view may open up new avenues of revenue
and provide creative solutions to opportunities that may have previously gone
unnoticed. It is envisaged that the greater the level of access smaller entities have
to data, the greater a range of options that can be provided for the consumer.
Beyond business, there is also added values to education, healthcare, or any
data-dependent institution. It will enable these institutions to share data in a
consensual manner. This increase in data will enable the institutions to increase
their understanding of the field they are operating within.
Two aspects of intended future work is outlined below:
Private Data The focus of this work is on achieving consensus between compet-
ing entities. Another element addressed was data privacy. Data privacy ensures
the adoption of the model, as it presents one less concern for parties who par-
ticipate. It also appeals to entities who would have benefited from multi-party
data exchanges previously but were restricted by their inability to share this
data, due to it containing sensitive information. Future developments of this
model will require data privacy. Privacy was ultimately a deciding factor in why
Hyperledger Fabric was chosen as the blockchain technology.
Federated Learning Machine learning is a rapidly growing field that is entirely
data-oriented. Access to larger and more varied data could generate more accu-
rate models. Achieving consensus between smaller entities could allow them to
bring more to the field of machine learning. Federated learning is an application
that could benefit from this design of data exchange model.
Institutions and business alike would benefit from this. The institutional
sharing of data would allow researchers to solve issues that would have been
previously restricted by access to data and the ability to share it.
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