Applying Differential Privacy to Matrix Factorization

Berlioz, Arnaud; Friedman, Arik; Kâafar, Mohamed Ali; Boreli, Roksana; Berkovsky, Shlomo

doi:10.1145/2792838.2800173

Cited by 109 publications

(74 citation statements)

References 16 publications

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“…Following the seminal work of Mc-Sherry and Mironov [19], researchers have tried to apply the differential privacy concept to the prevent information leakages from recommender outputs, e.g. [4,12,13].…”

Section: Examining the Dp-based Solutionsmentioning

confidence: 99%

Privacy-Preserving and yet Robust Collaborative Filtering Recommender as a Service

Tang

2020

Information Security and Cryptology

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Collaborative filtering recommenders provide effective personalization services at the cost of sacrificing the privacy of their end users. Due to the increasing concerns from the society and stricter privacy regulations, it is an urgent research challenge to design privacypreserving and yet robust recommenders which offer recommendation services to privacy-aware users. Our analysis shows that existing solutions fall short in several aspects, including lacking attention to the precise output to end users and ignoring the correlated robustness issues. In this paper, we provide a general system structure for latent factor based collaborative filtering recommenders by formulating them into model training and prediction computing stages, and also describe a new security model. Aiming at pragmatic solutions, we first show how to construct privacy-preserving and yet robust model training stage based on existing solutions. Then, we propose two cryptographic protocols to realize a privacy-preserving prediction computing stage, depending on whether or not an extra proxy is involved. Different from standard Topk recommendations, we alternatively let the end user retrieve the unrated items whose predictions are above a threshold, as a result of our privacy by design strategy. Experimental results show that our new protocols are quite efficient.

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Section: Examining the Dp-based Solutionsmentioning

confidence: 99%

Privacy-Preserving and yet Robust Collaborative Filtering Recommender as a Service

Tang

2020

Information Security and Cryptology

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“…We add Laplacian noises to ratings and train MF using SGLD on these perturbed ratings to derive ISGLD. The -differential privacy of adding Laplacian noises to ratings has been proved in [8]. We compare SDMF with ISGLD with privacy budget set to 4 and 2, while the former is the largest number we set for privacy budgets in SDMF and the latter is what reported to have comparable performance as item average baseline by [8].…”

Section: Datasets and Evaluation Settingsmentioning

confidence: 99%

Towards a more reliable privacy-preserving recommender system

Jiang

Lin

2019

Information Sciences

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This paper proposes a privacy-preserving distributed recommendation framework, Secure Distributed Collaborative Filtering (SDCF), to preserve the privacy of value, model and existence altogether. That says, not only the ratings from the users to the items, but also the existence of the ratings as well as the learned recommendation model are kept private in our framework. Our solution relies on a distributed client-server architecture and a two-stage Randomized Response algorithm, along with an implementation on the popular recommendation model, Matrix Factorization (MF). We further prove SDCF to meet the guarantee of Differential Privacy so that clients are allowed to specify arbitrary privacy levels. Experiments conducted on numerical rating prediction and one-class rating action prediction exhibit that SDCF does not sacrifice too much accuracy for privacy.

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“…Privacy has also been studied recently in the context of recommender systems (Berlioz et al, 2015;Nikolaenko et al, 2013;McSherry and Mironov, 2009;Friedman et al, 2016;Liu et al, 2015;Shen and Jin, 2014). This growing body of work has been concerned for the most part with differentially private recommender systems, and always with the aim of guaranteeing that the actual records used to train the system are not recoverable.…”

Section: Related Workmentioning

confidence: 99%

Privacy and Fairness in Recommender Systems via Adversarial Training of User Representations

Resheff¹,

Elazar

Shahar³

et al. 2019

Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods

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Latent factor models for recommender systems represent users and items as low dimensional vectors. Privacy risks of such systems have previously been studied mostly in the context of recovery of personal information in the form of usage records from the training data. However, the user representations themselves may be used together with external data to recover private user information such as gender and age. In this paper we show that user vectors calculated by a common recommender system can be exploited in this way. We propose the privacy-adversarial framework to eliminate such leakage of private information, and study the trade-off between recommender performance and leakage both theoretically and empirically using a benchmark dataset. An advantage of the proposed method is that it also helps guarantee fairness of results, since all implicit knowledge of a set of attributes is scrubbed from the representations used by the model, and thus can't enter into the decision making. We discuss further applications of this method towards the generation of deeper and more insightful recommendations.

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Applying Differential Privacy to Matrix Factorization

Cited by 109 publications

References 16 publications

Privacy-Preserving and yet Robust Collaborative Filtering Recommender as a Service

Privacy-Preserving and yet Robust Collaborative Filtering Recommender as a Service

Towards a more reliable privacy-preserving recommender system

Privacy and Fairness in Recommender Systems via Adversarial Training of User Representations

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