Investigation and Application of Differential Privacy in Bitcoin

Khalilov, Merve Can Kuş; Lévi, Albert

doi:10.1109/access.2022.3151784

Cited by 5 publications

(3 citation statements)

References 37 publications

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“…demonstrating the combination's effectiveness in privacy preservation, a key aspect of this study. [19] and Fan et al [20] explored the application of differential privacy in blockchain contexts, relevant to this study's emphasis on privacy-preserving techniques in blockchain systems [19] [20]. Xu et al [21] introduced a blockchain-enabled decentralized learning system [21], aligning with this research's focus on federated learning and privacy.…”

Section: Literature Reviewmentioning

confidence: 99%

PMNBARL: Enhancing Efficiency of Privacy Management in Digital Networks through Blockchain and Adaptive Reinforcement Learning

Research,

Bhaladhare,

Potgantwar

2024

Preprint

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The urgent need for advanced privacy mechanisms in the digital era is underscored by the growing concerns over data breaches and privacy invasions. Traditional privacy management methods often fall short in addressing these challenges, primarily due to their static nature and limited adaptability to evolving cyber threats. This work presents an efficient method to privacy management by integrating blockchain technology with adaptive privacy mechanisms, leveraging the power of machine learning (ML) and deep learning (DL) algorithms. Existing privacy management systems are predominantly rigid, offering limited scope for dynamic adaptation to changing network conditions and user behaviors. Such systems are increasingly inadequate in handling the complexities of modern data environments, often leading to compromised data confidentiality and higher instances of unauthorized data access or policy violations for different use cases. In response, this paper introduces an innovative model that employs smart contracts on the blockchain for privacy policy enforcement operations. These smart contracts ensure secure, transparent, and immutable privacy management, markedly enhancing data confidentiality and reducing policy violations under different attacks. Furthermore, the application of Reinforcement Learning (RL), enables dynamic privacy policy management operations. RL's ability to learn and adjust policies adaptively in response to environmental feedback ensures improved responsiveness and efficiency in privacy settings. A novel aspect of this work is the integration of Anomaly Detection using Deep Neural Networks (DNNs) with blockchain technology for self-adaptive security. DNNs' proficiency in identifying complex patterns in large datasets allows for the early detection of privacy breaches, enhancing the overall security performance levels. Additionally, the implementation of Differential Privacy in Federated Learning addresses the challenge of preserving data privacy during collective model training, thus ensuring robust privacy protection operations. The proposed methods were tested on various real-time simulation datasets, showcasing superior performance over existing methods in terms of energy efficiency, speed, throughput, consistency, and packet delivery ratio sets. This work not only presents a significant advancement in privacy management but also sets a new standard for future research in the field of data privacy and security levels.

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Section: Literature Reviewmentioning

confidence: 99%

PMNBARL: Enhancing Efficiency of Privacy Management in Digital Networks through Blockchain and Adaptive Reinforcement Learning

Research,

Bhaladhare,

Potgantwar

2024

Preprint

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“…The formula is shown in Equation (3) . Both the values of ϵ and δ are expected to be small enough to ensure string differential privacy [37] .

…”

Section: Privacy Analysismentioning

confidence: 99%

BRON: A blockchained framework for privacy information retrieval in human resource management

Kumar,

Saha,

Gupta

et al. 2024

Heliyon

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“…Numerous solutions [9,19,22,32,48] that encompass such areas as the use of ring signatures, smart contract mixing services, commitments schemes, and zeroknowledge proofs, among others, have been put forward. More recently, differential privacy [20] has been proposed; however, they all have diverse limitations. We refer the reader to a recent survey that elucidates on the aforementioned techniques [44].…”

Section: Introductionmentioning

confidence: 99%

Privacy Preservation for Transaction Initiators: Stronger Key Image Ring Signature and Smart Contract-Based Framework

Odoom

Huang²,

Danso³

et al. 2023

csci

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Recently, blockchain technology has garnered support. However, an attenuating factor to its global adoption in certain use cases is privacy-preservation owing to its inherent transparency. A widely explored cryptographic option to address this challenge has been ring signature which aside its privacy guarantee must be double spending resistant. In this paper, we identify and prove a catastrophic flaw for double-spending attack in a Lightweight Ring Signature scheme and proceed to construct a new, fortified commitment scheme using the signer’s entire private key. Subsequently, we compute a stronger key image to yield a double-spending-resistant signature scheme solidly backed by formal proof. Inherent in our solution is a novel, zero-knowledge-based, secured and cost-effective smart contract for public key aggregation. We test our solution on a private blockchain as well as Kovan testnet along with performance analysis attesting to efficiency and usability and make the code publicly available on GitHub.

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Investigation and Application of Differential Privacy in Bitcoin

Cited by 5 publications

References 37 publications

PMNBARL: Enhancing Efficiency of Privacy Management in Digital Networks through Blockchain and Adaptive Reinforcement Learning

PMNBARL: Enhancing Efficiency of Privacy Management in Digital Networks through Blockchain and Adaptive Reinforcement Learning

BRON: A blockchained framework for privacy information retrieval in human resource management

Privacy Preservation for Transaction Initiators: Stronger Key Image Ring Signature and Smart Contract-Based Framework

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