Differential privacy in networked data collection

Javidbakht, Omid; Venkitasubramaniam, Parv

doi:10.1109/ciss.2016.7460487

Cited by 3 publications

(2 citation statements)

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“…[15], [16]. Mutual information based privacy metrics have also been considered for data streaming applications [17]- [19]. Wang et al [20] compare mutual information privacy with differential privacy under Hamming distortion.…”

Section: B Related Workmentioning

confidence: 99%

Robust Privacy-Utility Tradeoffs Under Differential Privacy and Hamming Distortion

Kalantari

Sankar

Sarwate

2018

IEEE Trans.Inform.Forensic Secur.

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A privacy-utility tradeoff is developed for an arbitrary set of finite-alphabet source distributions. Privacy is quantified using differential privacy (DP), and utility is quantified using expected Hamming distortion maximized over the set of distributions. The family of source distribution sets (source sets) is categorized into three classes, based on different levels of prior knowledge they capture. For source sets whose convex hull includes the uniform distribution, symmetric DP mechanisms are optimal. For source sets whose probability values have a fixed monotonic ordering, asymmetric DP mechanisms are optimal. For all other source sets, general upper and lower bounds on the optimal privacy leakage are developed and a necessary and sufficient condition for tightness are established.Differentially private leakage is an upper bound on mutual information (MI) leakage: the two criteria are compared analytically and numerically to illustrate the effect of adopting a stronger privacy criterion.

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Section: B Related Workmentioning

confidence: 99%

Robust Privacy-Utility Tradeoffs Under Differential Privacy and Hamming Distortion

Kalantari

Sankar

Sarwate

2018

IEEE Trans.Inform.Forensic Secur.

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“…DynaEgo utilizes the principle of DP as well as the social relationships to adaptively modify the users' rating histories to prevent exact user information from being leaked. Javidbakht and Venkitasubramaniam [18] proposed using DP as a metric to quantify the privacy of the intended destination, and optimal probabilistic routing schemes are investigated under unicast and multicast paradigms. Balu and Furon [19] proposed using sketching techniques to implicitly provide DP guarantees by taking advantage of the inherent randomness of the data structure, and this approach is well suited for large-scale applications.…”

Section: Related Workmentioning

confidence: 99%

New Collaborative Filtering Algorithms Based on SVD++ and Differential Privacy

Xian

et al. 2017

Mathematical Problems in Engineering

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Collaborative filtering technology has been widely used in the recommender system, and its implementation is supported by the large amount of real and reliable user data from the big-data era. However, with the increase of the users’ information-security awareness, these data are reduced or the quality of the data becomes worse. Singular Value Decomposition (SVD) is one of the common matrix factorization methods used in collaborative filtering, which introduces the bias information of users and items and is realized by using algebraic feature extraction. The derivative model SVD++ of SVD achieves better predictive accuracy due to the addition of implicit feedback information. Differential privacy is defined very strictly and can be proved, which has become an effective measure to solve the problem of attackers indirectly deducing the personal privacy information by using background knowledge. In this paper, differential privacy is applied to the SVD++ model through three approaches: gradient perturbation, objective-function perturbation, and output perturbation. Through theoretical derivation and experimental verification, the new algorithms proposed can better protect the privacy of the original data on the basis of ensuring the predictive accuracy. In addition, an effective scheme is given that can measure the privacy protection strength and predictive accuracy, and a reasonable range for selection of the differential privacy parameter is provided.

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