Privacy preserving distributed optimization using homomorphic encryption

Lü, Yang; Zhu, Minghui

doi:10.1016/j.automatica.2018.07.005

Cited by 146 publications

(82 citation statements)

References 53 publications

(84 reference statements)

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“…It was reported that encrypting a bit plain-text Big Data constructs a huge amount of bits for cipher-texts, could extend millions of bits in some situations [15]. The estimated workload Big Data rapidly enlarge as the amount of evaluation trading operations was combined with the number of variables active in the calculation [13], [14]. In addition, our tensor-supported FHE was separate from the lattice-based homomorphic approach [12]developed a tensor-based FHE that applied a data holder-centric mechanism and empower all data to be encrypted in the primary Big Data while supporting accurate manipulations.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Homomorphic Re-Encryption using Laplacian for Preserving the Privacy in Big Data Analytics

2020

IJRTE

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 Abstract: Big data offers various services like storing sensitive, private data and maintaining the data. Big data users may upload encrypted data rather than raw data for preserving data. Processing and analyzing the encrypted data is the primary target for attackers and hackers. Homomorphic Re-Encryption to supports access control, processed cipher-text on encrypted data and ensure data confidentiality. However, the limitation of Homomorphic Re-Encryption is the single-user system, which means it allows the party that owns a homomorphic decryption key to decrypt processed cipher-texts. Original Homomorphic Re-Encryption cannot support multiple users to access the processed cipher texts flexibly. In this paper, propose a Privacy-Preserving Big Data Processing system which support of a Homomorphic Re-Encryption using laplacian phase that extends partially from a single-group user system by offering cipher text re-encryption that allows accessing processed cipher-texts. Through the cooperation of a Data Provider, to increase the flexibility and security of our system, However apply multiple Services to take in charge of the data from their users and design computing operations over cipher-texts belonging to multiple Service. The analysis completed on proves that our Preserving the Privacy of Big Data Processing method's to performance in terms of security is good on some datasets, inefficiency this also ensures the security and user privacy.

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Section: Related Workmentioning

confidence: 99%

Enhanced Homomorphic Re-Encryption using Laplacian for Preserving the Privacy in Big Data Analytics

2020

IJRTE

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“…Suwandi et al [83] explored the integration of encryption algorithms based on homomorphic properties to secure e-voting system and establish that Paillier and Okamoto Uchiyama algorithms can easily be integrated into e-voting systems to improve security. Moreover, Homomorphic Encryption is also potentially applicable to the domain of control and optimization, as evident from the two studies by Yang and Zhu [84]. In one of their studies, the researchers utilized HE in combination with reinforcement learning to devise a distributed algorithm that facilitated in preserving both, security and privacy of the operators and agents within discrete constrained games [84].…”

Section: Electronic Voting Applicationsmentioning

confidence: 99%

A systematic review on the status and progress of homomorphic encryption technologies

Alloghani

Alani

Al‐Jumeily

et al. 2019

Journal of Information Security and Applications

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With the emergence of big data and the continued growth in cloud computing applications, serious security and privacy concerns emerged. Consequently, several researchers and cybersecurity experts have embarked on a quest to extend data encryption to big data systems and cloud computing applications. As most cloud users turn to using public cloud services, confidentiality becomes and even more complicated issue. Cloud clients storing their data on a public cloud always seek solutions to confidentiality problem. Homomorphic encryption emerged as a possible solution where client's data is encrypted on the cloud in a way that allows some search and manipulation operations without proper decryption. In this paper, we present a systematic review of research paper published in the field of homomorphic encryption. This paper uses PRISMA checklist alongside some items of Cochrane's Quality Assessment to review studies retrieved from various resources. It was highly noticeable in the reviewed papers that security in big data and cloud computing has received most attention. Most papers suggested the use of homomorphic encryption although the thematic analysis has identified other potential concerns. Regarding the quality of the articles, 38% of the articles failed to meet three checklist items, including explicit statement of research objectives, procedure recognition and sources of funding used in the study. The review also presented compendium textual analysis of di erent homomorphic encryption algorithms, application areas, and areas of future developments. Results of the evaluation through PRISMA and the Cochrane tool showed that a majority of research articles discussed the potential use and application of Homomorphic Encryption as a solution to the growing demands of big data and absence of security and privacy mechanisms therein. This was evident from 26 of the total 59 articles that met the inclusion criteria. The term Homomorphic Encryption appeared 1802 times in the word cloud derived from the selected articles, which speaks of its potential to ensure security and privacy, while also preserving the CIA triad in the context of big data and cloud computing.

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“…In the control and CPS communities, differential privacy has been adopted to Kalman filtering (Le Ny and Pappas, 2014), consensus (Huang et al, 2012) and optimization (Nozari et al, 2016;Zhang et al, 2016); mutual information has been used as a privacy metric in the applications of smart grid (Han et al, 2016) and stochastic control systems (Venkitasubramaniam et al, 2015); semantic security and perfect secrecy have been employed in secure multiparty computation and homomorphic encryption (Lu and Zhu, 2018).…”

Section: Introductionmentioning

confidence: 99%

On privacy preserving data release of linear dynamic networks

Lü

Zhu

2020

Automatica

Self Cite

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Distributed data sharing in dynamic networks is ubiquitous. It raises the concern that the private information of dynamic networks could be leaked when data receivers are malicious or communication channels are insecure. In this paper, we propose to intentionally perturb the inputs and outputs of a linear dynamic system to protect the privacy of target initial states and inputs from released outputs. We formulate the problem of perturbation design as an optimization problem which minimizes the cost caused by the added perturbations while maintaining system controllability and ensuring the privacy. We analyze the computational complexity of the formulated optimization problem. To minimize the 0 and 2 norms of the added perturbations, we derive their convex relaxations which can be efficiently solved. The efficacy of the proposed techniques is verified by a case study on a heating, ventilation, and air conditioning system.

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Privacy preserving distributed optimization using homomorphic encryption

Cited by 146 publications

References 53 publications

Enhanced Homomorphic Re-Encryption using Laplacian for Preserving the Privacy in Big Data Analytics

Enhanced Homomorphic Re-Encryption using Laplacian for Preserving the Privacy in Big Data Analytics

A systematic review on the status and progress of homomorphic encryption technologies

On privacy preserving data release of linear dynamic networks

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