Towards a more reliable privacy-preserving recommender system

Jiang, Jia Yun; Li, Cheng-Te; Lin, Shou-De

doi:10.1016/j.ins.2018.12.085

Cited by 42 publications

(18 citation statements)

References 21 publications

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“…Based on traditional secure multi‐party computation techniques such as homomorphic encryption, Garbled circuit, or special implementation of privacy preserving operations, such as secure sums—representative works in the secure multiparty area include for instance 7,19,20 Based on a system that trades off privacy with accuracy (eg, differential privacy work 21 and the referenced papers therein, or through profile obfuscation 22 ) Based on an ElGamal scheme of homomorphic encryption 23 which works as follows: the multiplication of two cipher texts equals the encryption of the multiplication of the plain texts …”

Section: Related Workmentioning

confidence: 99%

Dare‐to‐Share: Collaborative privacy‐preserving recommendations with (almost) no crypto

Russo

Bracciale

Bianchi

2021

Security and Privacy

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Collaborative recommending systems aim to predict a potential user‐item rating on the basis of remaining ones. Since, in several contexts, sharing of other users' ratings may be prevented by confidentiality concerns, several works have effectively addressed the design of privacy preserving recommenders. Still, most of the proposed solutions rely on advanced cryptographic methodologies, whose may conflict with the simplicity and viability requirements of real world deployments. In contrast, we propose an approach which does not require any complex cryptography. We show that whenever we can tolerate recommendations based on average values, we can transform the recommender into a privacy‐preserving one, by using two noncolluding replicas of the same system, and by distributing randomly “blinded” data to these replicas. To protecting each user's rating, a key asset of our approach is the ability to conceal which specific items are rated by which users. Our proposal is secure under the honest‐but‐curious attacker's assumption, and we show how it can be extended to guarantee robustness also against malicious adversaries. Finally, as a proof‐of‐concept, we present an implementation of the proposed approach for our motivating use case—collaborative assessment of computer/network vulnerabilities without revealing which of them affect one own infrastructure.

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Section: Related Workmentioning

confidence: 99%

Dare‐to‐Share: Collaborative privacy‐preserving recommendations with (almost) no crypto

Russo

Bracciale

Bianchi

2021

Security and Privacy

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“…To the same direction end, privacy-preserving recommendation approaches aim at preserving consumers' privacy through hiding their rating feedback from servers and/or other consumers [177,178]. Fig.…”

Section: Privacy Preserving Recommendationsmentioning

confidence: 99%

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Himeur¹,

Alsalemi²,

Al‐Kababji³

et al. 2021

Preprint

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Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems' performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors' knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.

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“…Meanwhile, they used the dimensionality reduction technique to reduce domain space, which improves the data utility. Jiang et al [ 200 ] proposed a more reliable Secure Distributed Collaborative Filtering (SDCF) framework that ables to preserve the privacy of data items, recommendation model, and the existence of the ratings at the same time. Nonetheless, SDCF performs RAPPOR in each iteration to achieve LDP protection, which will lead to a large perturbation error and low accuracy.…”

Section: Applicationsmentioning

confidence: 99%

A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis

Wang

Zhang

Feng

et al. 2020

Sensors

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Collecting and analyzing massive data generated from smart devices have become increasingly pervasive in crowdsensing, which are the building blocks for data-driven decision-making. However, extensive statistics and analysis of such data will seriously threaten the privacy of participating users. Local differential privacy (LDP) was proposed as an excellent and prevalent privacy model with distributed architecture, which can provide strong privacy guarantees for each user while collecting and analyzing data. LDP ensures that each user’s data is locally perturbed first in the client-side and then sent to the server-side, thereby protecting data from privacy leaks on both the client-side and server-side. This survey presents a comprehensive and systematic overview of LDP with respect to privacy models, research tasks, enabling mechanisms, and various applications. Specifically, we first provide a theoretical summarization of LDP, including the LDP model, the variants of LDP, and the basic framework of LDP algorithms. Then, we investigate and compare the diverse LDP mechanisms for various data statistics and analysis tasks from the perspectives of frequency estimation, mean estimation, and machine learning. Furthermore, we also summarize practical LDP-based application scenarios. Finally, we outline several future research directions under LDP.

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Towards a more reliable privacy-preserving recommender system

Cited by 42 publications

References 21 publications

Dare‐to‐Share: Collaborative privacy‐preserving recommendations with (almost) no crypto

Dare‐to‐Share: Collaborative privacy‐preserving recommendations with (almost) no crypto

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis

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