Follow blockchain research across journals, conferences, and preprint repositories.
5,430 results · page 193 of 227
Geoffroy Couteau, Michael Reichle
No abstract is available for this record.
Elizabeth C. Crites, Anna Lysyanskaya
No abstract is available for this record.
Erik-Oliver Blaß, Florian Kerschbaum
We focus on securely computing the ranks of sealed integers distributed among n parties. For example, we securely compute the largest or smallest integer, the median, or in general the kth-ranked integer. Such computations are a useful building block to securely implement a variety of sealed-bid auctions. Our objective is efficiency, specifically low interactivity between parties to support blockchains or other scenarios where multiple rounds are time-consuming. Hence, we dismiss powerful, yet highly-interactive MPC frameworks and propose BOREALIS, a special-purpose protocol for secure computation of ranks among integers. BOREALIS uses additively homomorphic encryption to implement core comparisons, but computes under distinct keys, chosen by each party to optimize the number of rounds. By carefully combining cryptographic primitives, such as ECC Elgamal encryption, encrypted comparisons, ciphertext blinding, secret sharing, and shuffling, BOREALIS sets up systems of multi-scalar equations which we efficiently prove with Groth-Sahai ZK proofs. Therewith, BOREALIS implements a multi-party computation of pairwise comparisons and rank zero-knowledge proofs secure against malicious adversaries. BOREALIS completes in at most 4 rounds which is constant in both bit length l of integers and the number of parties n. This is not only asymptotically optimal, but surpasses generic constant-round secure multi-party computation protocols, even those based on shared-key fully homomorphic encryption. Furthermore, our implementation shows that BOREALIS is very practical. Its main bottleneck, ZK proof computations, is small in practice. Even for a large number of parties (n=200) and high-precision integers (l=32), computation time of all proofs is less than a single Bitcoin block interval.
Liwen Zheng, Xiaohan Helu, Mohan Li, Hui Lu
No abstract is available for this record.
Gang Xue, Jia Xu, Hanwen Wu, Weifeng Lu · 5 authors
No abstract is available for this record.
Chengjun Cai, Yifeng Zheng, Yuefeng Du, Zhan Qin · 5 authors
Public blockchains have emerged as a promising direction in revolutionizing existing data-driven systems relying on centralized service providers. Among others, one kind of such systems is the popular crowdsensing systems which promise convenient data collection and aggregation. Although promising, leveraging public blockchains to build crowdsensing systems is non-trivial and has to overcome several barriers. First, public blockchains are transparent and lack support for data privacy. Second, participants from the open blockchain environment may misbehave in serving crowdsensing applications, like providing invalid data or doing aggregation incorrectly. Further, on-chain processing incurs monetary cost, so simply putting all workload on-chain is highly uneconomical and a delicate joint on-chain and off-chain design is required. In this paper, we take the first research attempt and explore a new design point to bridge public blockchains with crowdsensing systems. We propose a framework for building private, robust, and verifiable blockchain-empowered crowdsensing systems. It features an open service paradigm where blockchain nodes can rent out their computing resources to serve crowdsensing applications, with custom and full-fledged mechanisms to foster a healthy and economical ecosystem and to simultaneously tackle the challenges of data privacy, robustness against misbehaving participants, and service correctness assurance. Extensive experiments demonstrate our designs practicality.
Antonio Tenorio-Fornés, Viktor Jacynycz, David Llop-Vila, Antonio A. Sánchez‐Ruiz · 5 authors
The current processes of scientific publication and peer review raise concerns around fairness, quality, performance, cost, and accuracy. The Open Access movement has been unable to fulfill all its promises, and a few middlemen publishers can still impose policies and concentrate profits. This paper, using emerging distributed technologies such as Blockchain and IPFS, proposes a decentralized publication system for open science. The proposed system would provide (1) a distributed reviewer reputation system, (2) an Open Access by-design infrastructure, and (3) transparent governance processes. A survey is used to evaluate the problems, proposed solutions and possible adoption resistances, while a working prototype serves as a proof-of-concept. Additionally, the paper discusses the implementation, in a distributed context, of different privacy settings for both open peer review and reputation systems, introducing a novel approach supporting both anonymous and accountable reviews. The paper concludes reviewing the open challenges of this ambitious proposal.
Lucas M. Palma, Fernanda O. Gomes, Martín Vigil, Jean Everson Martina
No abstract is available for this record.
Weifeng Hao, Jiajie Zeng, Xiaohai Dai, Jiang Xiao · 8 authors
No abstract is available for this record.
Prabhanjan Ananth, Apoorvaa Deshpande, Yael Tauman Kalai, Anna Lysyanskaya
In this work, we define and construct fully homomorphic non-interactive zero knowledge (FH-NIZK) and non-interactive witness-indistinguishable (FH-NIWI) proof systems.
Xudong Zhu, Hui Li, Yang Yu
No abstract is available for this record.
William R. Black, Ryan Henry
We present a new 4-move special honest-verifier zero-knowledge proof of knowledge system for proving that a vector of Pedersen commitments opens to a so-called one-hot'' vector (i.e., to a vector from the standard orthonormal basis) from Zpn. The need for such proofs arises in the contexts of symmetric private information retrieval (SPIR), end-to-end verifiable voting (E2E), and privacy-preserving data aggregation and analytics, among others. The key insight underlying the new protocol is a simple observation regarding the paucity of roots of polynomials of bounded degree over a finite field. The new protocol is fast and yields succinct proofs: For vectors of length n, the prover evaluates Θ(Θlgn) group operations plus Θ(n) field operations and sends just Θ(Θlgn) group and field elements, while the verifier evaluates one n-base multiexponentiation plus Θ(llgn) additional group operations and sends just 2(λ+lgn) bits to obtain a soundness error less than 2-λ. (A 5-move variant of the protocol reduces prover upload to just λlgn bits for the same soundness error.) We have implemented both our new protocol and its closest competitors from the literature; in accordance with our analytic results, experiments confirm that the new protocols handily outperform existing protocols for all but the shortest of vectors (roughly, for vectors with more than 16-32 elements).
Satrajit Ghosh, Tobias Nilges
No abstract is available for this record.
Αλεξάνδρα Γιαννοπούλου, Valeria Ferrari
Blockchains and the GDPR pursue similar objectives where they seek to grant users greater control over their personal data. While the latter pursues this goal by imposing duties of care to centralised controllers and collectors of data, blockchains go a step beyond by trying to eliminate these stakeholders and the need to trust them. Nevertheless, the rules set out by the GDPR apply whenever personal data are at stake, and various actors of the blockchain ecosystem risk liability for controlling of processing data in violation of privacy requirements. A possible solution is to re-contextualise the concepts of data controlling and responsibility, as framed by the GDPR, in light of blockchains’ enhanced individual autonomy. In this paper, we set the framework for a further inquiry on the role of users as both data subjects and data controllers of distributed ledgers.
Yiming Wu, Shaohua Tang, Bowen Zhao, Zhiniang Peng
Task matching in crowdsourcing is designed to provide convenient task information retrieval and has been extensively explored. In general, the task matching process is required to be reliable and to meet privacy requirements. However, most existing privacy-preserving task matching solutions for crowdsourcing focus on privacy issues but ignore the reliability of the process. In this paper, we propose a blockchain-based task matching scheme for crowdsourcing with a secure and reliable matching. Instead of utilizing a centralized cloud server, we employ smart contracts, an emerging blockchain technology, to provide reliable and transparent matching. In this way, data confidentiality and identity anonymity are achieved effectively and efficiently. The extensive privacy analysis and performance evaluation show that our solution is secure and feasible.
Jiasi Weng, Jian Weng, Jilian Zhang, Ming Li · 6 authors
Deep learning can achieve higher accuracy than traditional machine learning algorithms in a variety of machine learning tasks. Recently, privacy-preserving deep learning has drawn tremendous attention from information security community, in which neither training data nor the training model is expected to be exposed. Federated learning is a popular learning mechanism, where multiple parties upload local gradients to a server and the server updates model parameters with the collected gradients. However, there are many security problems neglected in federated learning, for example, the participants may behave incorrectly in gradient collecting or parameter updating, and the server may be malicious as well. In this article, we present a distributed, secure, and fair deep learning framework named DeepChain to solve these problems. DeepChain provides a value-driven incentive mechanism based on Blockchain to force the participants to behave correctly. Meanwhile, DeepChain guarantees data privacy for each participant and provides auditability for the whole training process. We implement a prototype of DeepChain and conduct experiments on a real dataset for different settings, and the results show that our DeepChain is promising.
Chiara Braghin, Stelvio Cimato, Simone Raimondi Cominesi, Ernesto Damiani · 5 authors
No abstract is available for this record.
Ke Zhao, Shaohua Tang, Bowen Zhao, Yiming Wu
Mobile crowdsensing (MCS) is an emerging data collection paradigm that exploits the potential of individual mobile devices to acquire mass data in a cost-effective manner. One of the important challenges in MCS application is to resist malicious users who provide false data to disturb the system. In the existing work, the reputation management scheme is an effective way to overcome the challenge. However, most reputation management schemes rely on a semi-honest server and process data in the plaintext domain without considering server security and user privacy. In this paper, we integrate the blockchain and edge computing in the MCS scenario to construct a credible and efficient blockchain-based MCS system, called BC-MCS. To resist malicious users, we present a privacy-preserving reputation management scheme based on the proposed system. Furthermore, we design a delegation protocol to solve the inherent problem of user dynamics in the MCS. The prototype system implemented on the Hyperledger Sawtooth and Android client demonstrates that our scheme can achieve higher utility and security levels in handling malicious users compared with the previous centralized reputation management schemes.
Rujia Li, David Galindo, Qi Wang
Anonymity revocation is an essential component of credential issuing systems since unconditional anonymity is incompatible with pursuing and sanctioning credential misuse. However, current anonymity revocation approaches have shortcomings with respect to the auditability of the revocation process. In this paper, we propose a novel anonymity revocation approach based on privacy-preserving blockchain-based smart contracts, where the code self-execution property ensures availability and public ledger immutability provides auditability. We describe an instantiation of this approach, provide an implementation thereof and conduct a series of evaluations in terms of running time, gas cost and latency. The results show that our scheme is feasible and efficient.
Carlos Molina-Jiménez, Ioannis Sfyrakis, Linmao Song, Danny, Hazem · 5 authors
The hype about Bitcoin has overrated the potential of smart contracts deployed on-blockchains (on-chains) and underrated the potential of smart contracts deployed on-Trusted Third Parties (on-TTPs). As a result, current research and development in this field is focused mainly on smart contract applications that use on-chain smart contracts. We argue that there is a large class of smart contract applications where on-TTP smart contracts are a better alternative. The problem with on-chain smart contracts is that the fully decentralised model and indelible append-only data model followed by blockchains introduces several engineering problems that are hard to solve. In these situations, the inclusion of a TTP (assuming that the application can tolerate its inconveniences) instead of a blockchain to host the smart contract simplifies the problems and offers pragmatic solutions. The intention and contribution of this paper is to shed some light on this issue. We use a hypothetical use case of a car insurance application to illustrate technical problems that are easier to solve with on-TTP smart contracts than with on-chain smart contracts.
Wei She, Zhihao Gu, Xukang Lyu, Qi Liu · 6 authors
The relative low level of smart home system (SHS) device information security may threaten the privacy of users. In this paper, we propose a homomorphic consortium blockchain for SHS sensitive data privacy preserving (HCB-SDPP), which is based on the traditional smart home system. We add verification services, which are composed of verification nodes, to our model to verify working nodes and transactions in SHS. In order to record the SHS device information transaction, we propose a new block data structure based on homomorphic encryption (HEBDS). Using the HCB-SDPP model, we design an encrypted algorithm based on Paillier encrypted for privacy protection. To verify the validity of the HCB-SDPP model, we firstly encrypt sensitive data of all gateway peers and upload them to the consortium blockchain. Then, we validate the security of sensitive data after homomorphic encryption processing. In the experiment, we also design attack experiments to attack different types of peers on the consortium blockchain in the HCB-SDPP model. If these nodes are insecure, the influence on the whole model will be analyzed. The simulation result shows that the HCB-SDPP model can protect customer privacy more effectively than SHS.
Md. Mehedi Hassan Onik, Chul‐Soo Kim, Nam Yong Lee, Jinhong Yang
Abstract Secure data distribution is critical for data accountability. Surveillance caused privacy breaching incidents have already questioned existing personal data collection techniques. Organizations assemble a huge amount of personally identifiable information (PII) for data-driven market analysis and prediction. However, the limitation of data tracking tools restricts the detection of exact data breaching points. Blockchain technology, an ‘immutable’ distributed ledger, can be leveraged to establish a transparent data auditing platform. However, Art. 42 and Art. 25 of general data protection regulation (GDPR) demands ‘right to forget’ and ‘right to erase’ of personal information, which goes against the immutability of blockchain technology. This paper proposes a GDPR complied decentralized and trusted PII sharing and tracking scheme. Proposed blockchain based personally identifiable information management system (BcPIIMS) demonstrates data movement among GDPR entities (user, controller and processor). Considering GDPR limitations, BcPIIMS used off-the-chain data storing architecture. A prototype was created to validate the proposed architecture using multichain. The use of off-the-chain storage reduces individual block size. Additionally, private blockchain also limits personal data leaking by collecting fast approval from restricted peers. This study presents personal data sharing, deleting, modifying and tracking features to verify the privacy of proposed blockchain based personally identifiable information management system.
Benoît Libert, Khoa Nguyen, Benjamin Hong Meng Tan, Huaxiong Wang
No abstract is available for this record.
Martin Schanzenbach, Thomas Kilian, Julian Schütte, Christian Banse
In this paper we present ZKlaims: a system that allows users to present attribute-based credentials in a privacy-preserving way. We achieve a zero-knowledge property on the basis of Succinct Non-interactive Arguments of Knowledge (SNARKs). ZKlaims allow users to prove statements on credentials issued by trusted third parties. The credential contents are never revealed to the verifier as part of the proving process. Further, ZKlaims can be presented non-interactively, mitigating the need for interactive proofs between the user and the verifier. This allows ZKlaims to be exchanged via fully decentralized services and storages such as traditional peer-to-peer networks based on distributed hash tables (DHTs) or even blockchains. To show this, we include a performance evaluation of ZKlaims and show how it can be integrated in decentralized identity provider services.