A Transaction-Level Model for Blockchain Privacy

Considerable work explores blockchain privacy notions. Yet, it usually employs entirely different models and notations, complicating potential comparisons. In this work, we use the Transaction Directed Acyclic Graph (TDAG) and extend it to capture blockchain privacy notions (PDAG). We give consistent definitions for untraceability and unlinkability. Moreover, we specify conditions on a blockchain system to achieve each aforementioned privacy notion. Thus, we can compare the two most prominent privacy-preserving blockchains -- Monero and Zcash, in terms of privacy guarantees. Finally, we unify linking heuristics from the literature with our graph notation and review a good portion of research on blockchain privacy

The Evolution of Embedding Metadata in Blockchain Transactions

The use of blockchains is growing every day, and their utility has greatly expanded from sending and receiving crypto-coins to smart-contracts and decentralized autonomous organizations. Modern blockchains underpin a variety of applications: from designing a global identity to improving satellite connectivity. In our research we look at the ability of blockchains to store metadata in an increasing volume of transactions and with evolving focus of utilization. We further show that basic approaches to improving blockchain privacy also rely on embedding metadata. This paper identifies and classifies real-life blockchain transactions embedding metadata of a number of major protocols running essentially over the bitcoin blockchain. The empirical analysis here presents the evolution of metadata utilization in the recent years, and the discussion suggests steps towards preventing criminal use. Metadata are relevant to any blockchain, and our analysis considers primarily bitcoin as a case study. The paper concludes that simultaneously with both expanding legitimate utilization of embedded metadata and expanding blockchain functionality, the applied research on improving anonymity and security must also attempt to protect against blockchain abuse.Comment: 9 pages, 6 figures, 1 table, 2018 International Joint Conference on Neural Network

Privacy Preservation & Security Solutions in Blockchain Network

Blockchain has seen exponential progress over the past few years, and today its usage extends well beyond cryptocurrencies. Its features, including openness, transparency, secure communication, difficult falsification, and multi-consensus, have made it one of the most valuable technology in the world. In most open blockchain platforms, any node can access the data on the blockchain, which leads to a potential risk of personal information leakage. So the issue of blockchain privacy and security is particularly prominent and has become an important research topic in the field of blockchain. This dissertation mainly summarizes my research on blockchain privacy and security protection issues throughout recent years. We first summarize the security and privacy vulnerabilities in the mining pools of traditional bitcoin networks and some possible protection measures. We then propose a new type of attack: coin hopping attack, in the case of multiple blockchains under an IoT environment. This attack is only feasible in blockchain-based IoT scenarios, and can significantly reduce the operational efficiency of the entire blockchain network in the long run. We demonstrate the feasibility of this attack by theoretical analysis of four different attack models and propose two possible solutions. We also propose an innovative hybrid blockchain crowdsourcing platform solution to settle the performance bottlenecks and various challenges caused by privacy, scalability, and verification efficiency problems of current blockchain-based crowdsourcing systems. We offer flexible task-based permission control and a zero-knowledge proof mechanism in the implementation of smart contracts to flexibly obtain different levels of privacy protection. By performing several tests on Ethereum and Hyperledger Fabric, EoS.io blockchains, the performance of the proposed platform consensus under different transaction volumes is verified. At last, we also propose further investigation on the topics of the privacy issues when combining AI with blockchain and propose some defense strategies

Link Before You Share: Managing Privacy Policies through Blockchain

With the advent of numerous online content providers, utilities and applications, each with their own specific version of privacy policies and its associated overhead, it is becoming increasingly difficult for concerned users to manage and track the confidential information that they share with the providers. Users consent to providers to gather and share their Personally Identifiable Information (PII). We have developed a novel framework to automatically track details about how a users' PII data is stored, used and shared by the provider. We have integrated our Data Privacy ontology with the properties of blockchain, to develop an automated access control and audit mechanism that enforces users' data privacy policies when sharing their data across third parties. We have also validated this framework by implementing a working system LinkShare. In this paper, we describe our framework on detail along with the LinkShare system. Our approach can be adopted by Big Data users to automatically apply their privacy policy on data operations and track the flow of that data across various stakeholders.Comment: 10 pages, 6 figures, Published in: 4th International Workshop on Privacy and Security of Big Data (PSBD 2017) in conjunction with 2017 IEEE International Conference on Big Data (IEEE BigData 2017) December 14, 2017, Boston, MA, US

Analyzing UTXO-Based Blockchain Privacy Threats

While blockchain technologies leverage compelling characteristics in terms of decentralization, immutability, and transparency, user privacy in public blockchains remains a fundamental challenge that requires particular attention. This is mainly due to the history of all transactions being accessible and available to anyone, thus making it possible for an attacker to infer data about users that is supposed to remain private. In this paper, we provide a threat model of possible privacy attacks on users utilizing the Bitcoin blockchain. To this end, we followed the LINDDUN GO methodology to identify threats and suggest possible mitigation

Cryptographic approaches for confidential computations in blockchain.

Blockchain technologies have been widely re- searched in the last decade, mainly because of the revolution they propose for different use cases. Moving away from centralized solutions that abuse their capabilities, blockchain looks like a great solution for integrity, transparency, and decentral- ization. However, there are still some problems to be solved, lack of privacy being one of the main ones. In this paper, we focus on a subset of the privacy area, which is confidentiality. Although users are increasingly aware of the importance of confidentiality, blockchain poses a barrier to the confidential treatment of data. We initiate the study of cryptographic confidential computing tools and focus on how these technologies can endow the blockchain with better capabilities, i.e., enable rich and versatile applications while pro- tecting users’ data. We identify Zero Knowledge Proofs, Fully Homomorphic Encryption, and Se- cure Multiparty Computation as good candidates to achieve this.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech