scholarly journals Fully Privacy-Preserving Distributed Optimization Based on Secret Sharing

2021 ◽  
Author(s):  
Nianfeng Tian ◽  
Qinglai Guo ◽  
Hongbin Sun ◽  
Xin Zhou
Keyword(s):  
Secret Sharing ◽  
Private Information ◽  
Data Exchange ◽  
Distributed Optimization ◽  
Critical Issue ◽  
Privacy Preserving ◽  
Collaborative Optimization ◽  
Third Party ◽  
Master Problem ◽  
Optimization Framework

With the increasing development of smart grid, multiparty cooperative computation between several entities has become a typical characteristic of modern energy systems. Traditionally, data exchange among parties is inevitable, rendering how to complete multiparty collaborative optimization without exposing any private information a critical issue. This paper proposes a fully privacy-preserving distributed optimization framework based on secure multiparty computation (SMPC) secret sharing protocols. The framework decomposes the collaborative optimization problem into a master problem and several subproblems. The process of solving the master problem is executed in the SMPC framework via the secret sharing protocols among agents. The relationships of agents are completely equal, and there is no privileged agent or any third party. The process of solving subproblems is conducted by agents individually. Compared to the traditional distributed optimization framework, the proposed SMPC-based framework can fully preserve individual private information. Exchanged data among agents are encrypted and no private information disclosure is assured. Furthermore, the framework maintains a limited and acceptable increase in computational costs while guaranteeing optimality. Case studies are conducted to demonstrate the principle of secret sharing and verify the feasibility and scalability of the proposed methodology. <br>

scholarly journals Fully Privacy-Preserving Distributed Optimization Based on Secret Sharing

2021 ◽  
Author(s):  
Nianfeng Tian ◽  
Qinglai Guo ◽  
Hongbin Sun ◽  
Xin Zhou
Keyword(s):  
Secret Sharing ◽  
Private Information ◽  
Data Exchange ◽  
Distributed Optimization ◽  
Critical Issue ◽  
Privacy Preserving ◽  
Collaborative Optimization ◽  
Third Party ◽  
Master Problem ◽  
Optimization Framework

With the increasing development of smart grid, multiparty cooperative computation between several entities has become a typical characteristic of modern energy systems. Traditionally, data exchange among parties is inevitable, rendering how to complete multiparty collaborative optimization without exposing any private information a critical issue. This paper proposes a fully privacy-preserving distributed optimization framework based on secure multiparty computation (SMPC) secret sharing protocols. The framework decomposes the collaborative optimization problem into a master problem and several subproblems. The process of solving the master problem is executed in the SMPC framework via the secret sharing protocols among agents. The relationships of agents are completely equal, and there is no privileged agent or any third party. The process of solving subproblems is conducted by agents individually. Compared to the traditional distributed optimization framework, the proposed SMPC-based framework can fully preserve individual private information. Exchanged data among agents are encrypted and no private information disclosure is assured. Furthermore, the framework maintains a limited and acceptable increase in computational costs while guaranteeing optimality. Case studies are conducted to demonstrate the principle of secret sharing and verify the feasibility and scalability of the proposed methodology. <br>

scholarly journals Privacy-Preserving Dynamic Personalized Pricing with Demand Learning

2021 ◽  
Author(s):  
Xi Chen ◽  
David Simchi-Levi ◽  
Yining Wang
Keyword(s):  
Dynamic Pricing ◽  
Demand Function ◽  
Differential Privacy ◽  
Personal Information ◽  
Big Data Analytics ◽  
Information Leakage ◽  
Critical Issue ◽  
Privacy Preserving ◽  
Third Party ◽  
Privacy Concern

The prevalence of e-commerce has made customers’ detailed personal information readily accessible to retailers, and this information has been widely used in pricing decisions. When using personalized information, the question of how to protect the privacy of such information becomes a critical issue in practice. In this paper, we consider a dynamic pricing problem over T time periods with an unknown demand function of posted price and personalized information. At each time t, the retailer observes an arriving customer’s personal information and offers a price. The customer then makes the purchase decision, which will be utilized by the retailer to learn the underlying demand function. There is potentially a serious privacy concern during this process: a third-party agent might infer the personalized information and purchase decisions from price changes in the pricing system. Using the fundamental framework of differential privacy from computer science, we develop a privacy-preserving dynamic pricing policy, which tries to maximize the retailer revenue while avoiding information leakage of individual customer’s information and purchasing decisions. To this end, we first introduce a notion of anticipating [Formula: see text]-differential privacy that is tailored to the dynamic pricing problem. Our policy achieves both the privacy guarantee and the performance guarantee in terms of regret. Roughly speaking, for d-dimensional personalized information, our algorithm achieves the expected regret at the order of [Formula: see text] when the customers’ information is adversarially chosen. For stochastic personalized information, the regret bound can be further improved to [Formula: see text]. This paper was accepted by J. George Shanthikumar, big data analytics.

Privacy-Preserving Protocol for Atomic Swap Between Blockchains

2020 ◽  
Author(s):  
Kiran Gurung
Keyword(s):  
Secret Sharing ◽  
Privacy Preserving ◽  
The Other ◽  
Third Party ◽  
Fair Exchange ◽  
Trusted Third Party ◽  
Generic Framework ◽  
Ring Signatures ◽  
Successful Execution

Atomic swap facilitates fair exchange of cryptocurrencies without the need for a trusted authority. It is regarded as one of the prominent technologies for the cryptocurrency ecosystem, helping to realize the idea of a decentralized blockchain introduced by Bitcoin. However, due to the heterogeneity of the cryptocurrency systems, developing efficient and privacy-preserving atomic swap protocols has proven challenging. In this thesis, we propose a generic framework for atomic swap, called PolySwap, that enables fair ex-change of assets between two heterogeneous sets of blockchains. Our construction 1) does not require a trusted third party, 2) preserves the anonymity of the swap by preventing transactions from being linked or distinguished, and 3) does not require any scripting capability in blockchain. To achieve our goal, we introduce a novel secret sharing signature(SSSig) scheme to remove the necessity of common interfaces between blockchains in question. These secret sharing signatures allow an arbitrarily large number of signatures to be bound together such that the release of any single transaction on one blockchain opens the remaining transactions for the other party, allowing multi-chain atomic swaps while still being indistinguishable from a standard signature. We provide construction details of secret sharing signatures for ECDSA, Schnorr, and CryptoNote-style Ring signatures. Additionally, we provide an alternative contingency protocol, allowing parties to exchange to and from blockchains that do not support any form of time-locked escape transactions. A successful execution of PolySwap shows that it takes 8.3 seconds to complete an atomic swap between Bitcoin's Testnet3 and Ethereum's Rinkeby (excluding confirmation time).

scholarly journals Privacy-Preserving Fingerprint Authentication Using D-H Key Exchange and Secret Sharing

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Huiyong Wang ◽  
Mingjun Luo ◽  
Yong Ding
Keyword(s):  
Secret Sharing ◽  
Private Information ◽  
Data Privacy ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Key Exchange ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Biometric Data ◽  
Diffie Hellman

Biometric based remote authentication has been widely deployed. However, there exist security and privacy issues to be addressed since biometric data includes sensitive information. To alleviate these concerns, we design a privacy-preserving fingerprint authentication technique based on Diffie-Hellman (D-H) key exchange and secret sharing. We employ secret sharing scheme to securely distribute fragments of critical private information around a distributed network or group, which softens the burden of the template storage center (TSC) and the users. To ensure the security of template data, the user’s original fingerprint template is stored in ciphertext format in TSC. Furthermore, the D-H key exchange protocol allows TSC and the user to encrypt the fingerprint template in each query using a random one-time key, so as to protect the user’s data privacy. Security analysis indicates that our scheme enjoys indistinguishability against chosen-plaintext attacks and user anonymity. Through experimental analysis, we demonstrate that our scheme can provide secure and accurate remote fingerprint authentication.

ILSSPP: Intelligent location sharing system with privacy preserving features for smart cities

2021 ◽  
pp. 1-12
Author(s):  
Gokay Saldamli ◽  
Richard Chow ◽  
Hongxia Jin
Keyword(s):  
Service Providers ◽  
Smart Cities ◽  
User Interaction ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Third Party ◽  
Location Information ◽  
Fine Grained ◽  
Location Sharing ◽  
Social Network Structure

Social networking services are increasingly accessed through mobile devices. This trend has prompted services such as Facebook and Google+to incorporate location as a de facto feature of user interaction. At the same time, services based on location such as Foursquare and Shopkick are also growing as smartphone market penetration increases. In fact, this growth is happening despite concerns (growing at a similar pace) about security and third-party use of private location information (e.g., for advertising). Nevertheless, service providers have been unwilling to build truly private systems in which they do not have access to location information. In this paper, we describe an architecture and a trial implementation of a privacy-preserving location sharing system called ILSSPP. The system protects location information from the service provider and yet enables fine grained location-sharing. One main feature of the system is to protect an individual’s social network structure. The pattern of location sharing preferences towards contacts can reveal this structure without any knowledge of the locations themselves. ILSSPP protects locations sharing preferences through protocol unification and masking. ILSSPP has been implemented as a standalone solution, but the technology can also be integrated into location-based services to enhance privacy.

scholarly journals Multilevel Privacy Assurance Evaluation of Healthcare Metadata

2021 ◽  
Vol 11 (22) ◽  
pp. 10686
Author(s):  
Syeda Amna Sohail ◽  
Faiza Allah Bukhsh ◽  
Maurice van Keulen
Keyword(s):  
Privacy Preservation ◽  
Process Mining ◽  
Research Work ◽  
Healthcare Providers ◽  
Conceptual Modeling ◽  
Empirical Evaluation ◽  
Healthcare Services ◽  
Privacy Preserving ◽  
Third Party ◽  
Conformance Checking

Healthcare providers are legally bound to ensure the privacy preservation of healthcare metadata. Usually, privacy concerning research focuses on providing technical and inter-/intra-organizational solutions in a fragmented manner. In this wake, an overarching evaluation of the fundamental (technical, organizational, and third-party) privacy-preserving measures in healthcare metadata handling is missing. Thus, this research work provides a multilevel privacy assurance evaluation of privacy-preserving measures of the Dutch healthcare metadata landscape. The normative and empirical evaluation comprises the content analysis and process mining discovery and conformance checking techniques using real-world healthcare datasets. For clarity, we illustrate our evaluation findings using conceptual modeling frameworks, namely e3-value modeling and REA ontology. The conceptual modeling frameworks highlight the financial aspect of metadata share with a clear description of vital stakeholders, their mutual interactions, and respective exchange of information resources. The frameworks are further verified using experts’ opinions. Based on our empirical and normative evaluations, we provide the multilevel privacy assurance evaluation with a level of privacy increase and decrease. Furthermore, we verify that the privacy utility trade-off is crucial in shaping privacy increase/decrease because data utility in healthcare is vital for efficient, effective healthcare services and the financial facilitation of healthcare enterprises.

scholarly journals Privacy-preserving energy storage sharing with blockchain and secure multi-party computation

2021 ◽  
Vol 1 (1) ◽  
pp. 32-50
Author(s):  
Nan Wang ◽  
Sid Chi-Kin Chau ◽  
Yue Zhou
Keyword(s):  
Energy Storage ◽  
Energy Demand ◽  
Empirical Evaluation ◽  
Privacy Preserving ◽  
Third Party ◽  
User Privacy ◽  
External Energy ◽  
Novel Approach ◽  
Significant Cost Reduction ◽  
The Cost

Energy storage provides an effective way of shifting temporal energy demands and supplies, which enables significant cost reduction under time-of-use energy pricing plans. Despite its promising benefits, the cost of present energy storage remains expensive, presenting a major obstacle to practical deployment. A more viable solution to improve the cost-effectiveness is by sharing energy storage, such as community sharing, cloud energy storage and peer-to-peer sharing. However, revealing private energy demand data to an external energy storage operator may compromise user privacy, and is susceptible to data misuses and breaches. In this paper, we explore a novel approach to support energy storage sharing with privacy protection, based on privacy-preserving blockchain and secure multi-party computation. We present an integrated solution to enable privacy-preserving energy storage sharing, such that energy storage service scheduling and cost-sharing can be attained without the knowledge of individual users' demands. It also supports auditing and verification by the grid operator via blockchain. Furthermore, our privacy-preserving solution can safeguard against a majority of dishonest users, who may collude in cheating, without requiring a trusted third-party. We implemented our solution as a smart contract on real-world Ethereum blockchain platform, and provided empirical evaluation in this paper 1 .

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