=Paper=
{{Paper
|id=Vol-3031/paper5
|storemode=property
|title=Survey on Blockchain Privacy Challenges
|pdfUrl=https://ceur-ws.org/Vol-3031/paper_5.pdf
|volume=Vol-3031
|authors=Constance Hendrix, Rory Lewis
|dblpUrl=https://dblp.org/rec/conf/ifip8-1/HendrixL21
}}
==Survey on Blockchain Privacy Challenges==
https://ceur-ws.org/Vol-3031/paper_5.pdf
Survey on Blockchain Privacy Challenges
Constance Hendrix and Rory Lewis
University of Colorado, Colorado Springs CO 80919, USA
Abstract. Blockchain is the underlying technology behind cryptocur-
rency and is migrating quickly to other industry applications. Given the
rapid growth of Bitcoin and Ethereum, advances in blockchain has pro-
liferated; however, privacy threats still linger. This persistent concern
has spawned continuing analysis of existing and emerging threats and
innovative pathways for solutions to blockchain in cryptocurrency and
smart contracts. In addition, the critical issue of increasing the scalabil-
ity of blockchain without trading decentralization and security contin-
ues to challenge researchers. Herein, we present a concise analysis of the
blockchain process, detail categorical privacy vulnerabilities with notable
key solutions, discuss the emergence of oracle systems, then highlight
promising directions for future research.
Keywords: Blockchain · Privacy · Oracles.
1 Introduction
Blockchain is the foundational concept behind the cryptocurrency trendsetter,
Bitcoin. Its debut was made by Satoshi Nakamoto, whose work built upon con-
cepts introduced as early as the 1980s [13]. Since then, cryptocurrency options
have become forefront in the debate to replace fiat currencies, blockchain tech-
nology has transitioned to other industries, and advancements have been made
with security; however, problems with privacy, scalability, digital wallets, smart
contract, decentralized applications (dApps), and exchange security still exist
[36] [16] [28]. In industries where personal identifiable information (PII) is uti-
lized, and the risk of identity theft is viable, it is imperative that privacy be
prioritized. The threat is not limited to the blockchain itself, but off-chain stor-
age and external systems which interface with oracles to support smart contract
execution. Given this, additional vulnerabilities exist in terms of transaction pri-
vacy and reputation manipulation. In this paper, we perform a literature review
which starts broad, inspecting the area of privacy, then focus promising research
directions, which will inform the scope of and targets for future testing.
2 Blockchain Basics
The core of Bitcoin public blockchain is its ledger, which is openly distributed via
a peer-to-peer (P2P) network. Because P2P consensus is central to the system,
disrupting data integrity is difficult. A crypto transaction starts when an end
Copyright c 2021 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
55
user i) creates a digital wallet [22], and ii) digitally signs a transaction to send to
the blockchain. The blockchain is, in essence, a digital ledger that keeps track of
each crypto transaction and as it is duplicated and distributed across the entire
network of computer systems, some of which are operated by miners [38]. Miners
collect the transactions, add them to a block, then determine the block’s hash
puzzle solution, known as a nonce, which is their Proof of Work (PoW) [8]. While
the first miner publishes the block, which includes the nonce, block number, pre-
vious block hash, timestamp, and merkle root hash, other miners may follow by
publishing a block containing these same transactions, potentially creating mul-
tiple forks. Herein, timeliness is critical and having a trusted clock is exceedingly
important [25]. The miner who successfully attributes the most cumulative work
to the blockchain wins the reward and transaction fee [35], while the forks cre-
ated by other miners will be orphaned. For Bitcoin, consensus within the P2P
network follows a democratic validation schema and occurs every 10 minutes,
solidifying the group’s resulting determination. Essentially, all nodes, including
miners, have a unifying view of the blockchain and can view and verify all on-
going changes. The ledger uses hash functions to create fixed length hashes given
any arbitrary length of information which provide a check of the integrity in the
published block. Hash functions such as SHA256, are publicly known, but deriv-
ing information strictly from its hash is computationally non-trivial. Each block
not only contains its own hash, it also contains the previous block’s hash, hash
pointer, along with the Merkle root hash. The SHA256 hash used by Bitcoin is
of the Merkle Damgård Construction, which pads the information, to create the
root hash, divides the hash into blocks using the hash function f : 0, 1|bits| , then
compresses the data to a fixed length. In contrast, KECCAK256, used by Ethereum,
is of the Sponge construction, which works via absorb-and-squeeze-operations on
the data. Regardless of what algorithm is used, the retrieving validated infor-
mation is always embedded in the block and is dependent upon i) the nonce
discovery, ii) the hash of the previous block, and iii) the Merkle root determi-
nation. Given that a nonce is b length, each miner is mandated to check all 2b
combinations until a solution is found that allows the unlocking of the informa-
tion. The inclusion of the previous block’s hash provides the link in the chain,
while the Merkle Tree method generates a Merkle root hash of all hashes within
the block which validates the block and improves blockchain scalability [22]. The
essence of a Merkle tree as it travels over four transactions within a block can
be modeled as, Hmr = H (H(H(m1 )||H(m2 ))||(H(m3 )||H(m4 )), where Hmr is
the Merkle root hash, H is the hash function, || indicates concatenation, and
m indicates the message contained therein [27]. As blocks are published using
Bitcoin, despite the identity being somewhat protected, the details of the trans-
action are made available in various public blockchain implementations such as
"blockchain.com/explorer".
56
3 Privacy Vulnerabilities
The debate between privacy versus the attribution for illegal activities such as
tax evasion [16] and the now extinct Silk Road [28] continues. Regardless, the
need to protect personal identity and activities, which is sub-categorical to “se-
curity”, remains relevant. In the US, Nolo’s Plain-English Law Dictionary defines
privacy as the “the right not to have one’s personal matters disclosed or publi-
cized; the right to be left alone,” [17] while European Commission Regulation
2018/1725 addresses privacy through the protection of personal data. These two
referenced understandings provide the scope of privacy for this paper: protection
of individual’s activities, correspondence, identity, and personal records. Given
this scope, four areas within the field of blockchain are investigated, with the
takeaways provided up front:
1. Private keys - key discovery due to poorly randomized key construction [12]
or using quantum computers [23]
2. Transactions - identity discovery through network analysis of transaction
patterns using network analysis and behavior-based clustering [40]
3. Personal Records - compromise of personal identifiable information [34]
4. Smart Contracts - protection of user activity due to malicious manipulation
of external data sources (i.e., oracles) [28]
3.1 Private Keys
Asymmetric encryption is primarily used with cryptocurrency systems such as
Bitcoin and Ethereum; it is also used with private and consortium blockchains.
Zhang et al., proposed an e-Health system using a public key encryption in a
private blockchain [41]. Other applications relying on asymmetric encryption to
ensure privacy include but are not limited to voting systems[33], crowdsourcing
systems [29], and information retrieval systems [4], which is why preventing key
compromise is essential. Additionally, digital wallet privacy breeches can result
in lost assets [36] when the end user’s private key is lost in emails and or when
user names are physically lost. Three other approaches to protect these keys
are to use: 1) an additional layer of security on the user’s device; 2) biomet-
rics to prevent unauthorized access to the key; and, 3) biometrics in “known
cryptography algorithms” [9]. However, it is worth mentioning that biometrics
is also considered identifiable data, therfore should be protected as well. In ad-
dition, [16] highlighted companies similar to Chainalysis, Elliptic, and DMG,
whose business is to discover a user’s identity through digital wallet addresses.
Keeping wallets offline instead of online also reduces the risk of key compromise.
The key algorithm used by Bitcoin and Ethereum is the 256-bit Elliptic Curve
Digital Signature Algorithm (ECDSA), specifically secp256k1 [12]. ECDSA’s
Elliptic Curve enhancement is used with more foundational algorithms to reduce
computing power, which is favorable for implementation on mobile devices [39].
Although ECDSA seems to be secure, vulnerabilities do exist [3]. The Schnorr
signature, which unlike ECDSA, is linear and more conducive to applications
57
such as i) the Naïve Signature Aggregation where a user’s private key is part of a
collective signature used ultimately to sign a transaction, and ii) trusted external
data feeds, supplied by oracle systems later discussed. However, it is vulnerable
to compromise using a rouge key attack [31]. Multisignatures, which allow a
group of users to sign a single document and may have alleviated the Schnorr
vulnaribility by leveraging key interaction or challenges [10], [31].
3.2 Transactions
The goal of transaction privacy is to protect privacy of transaction contents
such as time, currency amount, and addresses from unauthorized entities [19].
To counter, a popular method for increasing transaction transparency is to ana-
lyze patterns within the blockchain network by using behavior-based clustering
techniques, such as k-means, then providing cluster definitions and rules in or-
der to characterize human behavior in the post-analysis. Supervised learning in
conjunction with k-means has been used to more accurately define clusters [6].
Silhouette scores have also been used with clustering to determine sufficiency of
each object’s classification [19]. In addition, other methods used to compromise
transaction security include transaction “fingerprints”, pattern determination,
network traffic analysis using mass data collection, and transaction propagation
techniques [19] [26]. For example Biryukov et al., performed a transaction propa-
gation analysis to link transactions and a method “for linking transaction clusters
to IP addresses of [initiating] nodes” [11]. Additionally, transaction propagation
within a system is often defined by the software used to conduct cryptocurrency
transactions and ledger updates such as advertisement-based propagation, send-
headers propagation, unsolicited push propagation, relay network propagation,
and push/advertisement hybrid propagation [28].
In light of these efforts, many solutions for protecting transaction privacy
have been devised [23]; which includes mixing and anonymous solutions [24], [19].
Mixing obfuscates transactions by mixing and re-distributing, while Anonymous
removes transaction payment origins. Other solutions comparable are also avail-
able. For example, the system Hawk addresses the issue by providing end users
the capability to privately create and interact with smart contracts using zero
knowledge proof protocols, then further protects data by storing transactional
data off-chain [25].
Block synchronization may also differ between systems. In 2015, Bitcoin up-
dated its broadcasting protocol from “trickle” to “diffusion” spreading propaga-
tion protocol which defined the delays between transaction transmission to the
nodes, neglecting to significantly improve the lack of anonymity in its network,
as in the case of [18]. In the event of system updates or attacks, such as smart
contract hack on Decentralized Autonomous Organization (DAO) venture capi-
tal fund [37], ledger inconsistencies between nodes can be introduced. The Proof
of Communication consensus-based solution, controls timing and claims to be a
more secure option over PoW and Proof of Stake (PoS), later discussed [15].
Government entities are also targeting law breakers. For example in effort to
enforce tax laws, the US’s Internal Revenue Service has a history of subpoenaing
58
companies owning cryptocurrency system [5] and hiring others equivalent to
Chainalysis to assist in the investigation of illegal activity. Michael Gronager,
CEO of Chainalysis, stated Chainalysis “builds personas around the transaction
patterns, then attributes them to entities” [14]. Although Chainalysis is arguably
a means for for a safer tomorrow, compromise of privacy is the price.
3.3 Personal Records
Although there is no precedence for PII in public blockchains, private and con-
sortium, quasi-private, blockchain implementations may include identification
authentication to ensure access to information, and transaction initiation is more
controlled. PII could also be included as transactional data, which include, but
are not limited to, biometrics, medical records, or government issued identifica-
tion. Compromise of identity due to authentication is covered in [34]. In addition
to authentication, PII will most likely be included in e-Health records and sys-
tems supporting real estate, insurance, public benefit, and voting management.
Although the restricted access or private and consortium blockchains are more
efficient compared to the public, and minimum privilege is a traditional security
strategy, there are privacy risks associated with these options [42].
3.4 Smart Contracts
The Smart Contract, initially proposed by Nick Szabo in 1994, was first in-
tegrated to support digital currency by Ethereum. A smart contract used on
blockchain with dAPP, minus the front-end user interface, is executable code
that is task dependent on external data from a trusted sources called oracles
and other pre-defined conditions. Ethereum, being the first to use this concept
on a public blockchain, relies on programming languages such as Solidity or
Vyper to create smart contracts to be executed on the Ethereum Virtual Ma-
chine (EVM). After the code is compiled and executed on EVM, it is broadcasted
to all nodes with access where end-users execute contracts by submitting a trans-
action. Although Ethereum was the first to implement smart contracts, smart
contracts are also used by others like HyperLedger, which uses a peer-designated
verification system and secures chaincode in a Docker container. For contracts
in general, privacy concerns creep into security of contract data fields designated
as “private”, data source authenticity, and vulnerabilities of trusted data feeds [7]
[32] [19]. Public blockchains housing these contracts maintain secrecy by using
cryptographic techniques, heavily relying on the robustness of the keys or rules,
but may be compromised depending on transaction behaviors [7]. Using external
data in contracts while maintaining privacy is arguably more challenging. Ora-
cle schemes, such as voting-based systems such as Chainlink, provide this service
but doesn’t actually authenticate the data nor invoke Transport Layers Security
(TLS) using third party verification [32]. Although the following section elabo-
rates this service, other proposed solutions, ranging in maturity, are noteworthy:
Ziraffe, Enigma Secret Contracts [43], and Town Crier [23].,
59
4 Oracle Systems Emergence
To expound, oracles are systems used to facilitate the use of trusted external data
in contracts and are provided to many blockchains, to include Bitcoin, Ethereum,
and HyperLedger. Consider ChainLink, which is comprised of a decentralized or-
acle system residing on Ethereum, whose functions can be divided into one of two
categories: on-chain and off-chain. On-chain is the label assigned to those func-
tions which have a direct interface with the blockchain ledger, whereas off-chain
functions do not. Data Providers, such as Binance, GamesScoreKeeper, and Am-
berdata, are external to the system, but provide necessary external data needed
by the system for the service to work. To explain, Fig. 1 steps through Chain-
Link’s process, depicting the interactions within and between [1]. The on-chain
ChainLink Smart Contract acts as the interface between the blockchain’s smart
contract and the off-chain ChainLink node, routing requests and data. Recently,
ChainLink announced its movement from using FluxAggregator for aggregating
data on-chain to off-chain, improving scalability of increasing data needs [21] and
reducing gas costs related to publishing data on Ethereum. Coined by Chain-
Link, “Off-Chain Reporting” (OCR) reports and digitally signs observations into
a single report, then sends it to the chain for a smart contract signature verifi-
cation [20]. To prevent data providers from sending false data to the system, its
trust model requires the provider to submit a stake in their native LINK token,
Chainlink’s proprietary Ethereum token. If data provided is deemed truthful, a
reward is administered; if not, a penalty to the stake is applied.
Fig. 1. ChainLink High-Level Functional Diagram is divided into three layers.
A specific oracle trust model defines its process to increase privacy protection
of external data. However, the risk of corrupted external data feeds, reputation
manipulation such as a Sybil attack, collusion between oracles, or identity thefts
exist [2]. Al-Breiki et al identified a plurality of trust models, where systems are
grouped and linked to its adopted trust model. These models are categorized as
on-chain, off-chain, and on-/off-chain, a hybrid approach. On-going research is
being conducted to improve existing methods, but challenges still exist. [32] [2].
60
5 Conclusion
Although cryptocurrencies are successful in contributing to value exchange, they
still have to overcome the distrust in the process from those who believe it rep-
resents another tulip bubble [30]. Regardless, markets leveraging blockchain are
growing and applications involving different industry sectors, to include edge
computing, are taking hold. Therefore, establishing robust privacy and security
techniques and practices should be a priority in future design. Recent surveys
have covered a variety of topics from blockchain malicious attacks to solutions
in disparate industries. However, surveys on private and consortium blockchain
privacy issues, papers providing detailed comparison of oracle security in current
implementations, and techniques determined to be state-of-the-art were more of
a challenge to locate. In this paper, we provided an overview of blockchain, then
investigated privacy vulnerabilities within four areas. In future work, we plan
to focus our efforts to create privacy-centric, scalable solutions to address cor-
rupted external data feeds and reputation manipulation attacks targeting oracle
systems, while identifying current state-of-the art methods and open challenges.
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