Conservative or liberal? Personalized differential privacy

Jorgensen, Zach; Yu, Ting; Cormode, Graham

doi:10.1109/icde.2015.7113353

Cited by 159 publications

(163 citation statements)

References 31 publications

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“…Fan and Jin [17] as well as Jorgensen et al [32] have used non-uniform random sampling to produce aggregate data. Hong et al have used random sampling for protecting search logs [24].…”

Section: Related Workmentioning

confidence: 99%

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SafePub: A Truthful Data Anonymization Algorithm With Strong Privacy Guarantees

Bild

Kuhn

Praßer

2018

Proceedings on Privacy Enhancing Technologies

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Methods for privacy-preserving data publishing and analysis trade off privacy risks for individuals against the quality of output data. In this article, we present a data publishing algorithm that satisfies the differential privacy model. The transformations performed are truthful, which means that the algorithm does not perturb input data or generate synthetic output data. Instead, records are randomly drawn from the input dataset and the uniqueness of their features is reduced. This also offers an intuitive notion of privacy protection. Moreover, the approach is generic, as it can be parameterized with different objective functions to optimize its output towards different applications. We show this by integrating six well-known data quality models. We present an extensive analytical and experimental evaluation and a comparison with prior work. The results show that our algorithm is the first practical implementation of the described approach and that it can be used with reasonable privacy parameters resulting in high degrees of protection. Moreover, when parameterizing the generic method with an objective function quantifying the suitability of data for building statistical classifiers, we measured prediction accuracies that compare very well with results obtained using state-ofthe-art differentially private classification algorithms.

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“…Fan and Jin [17] as well as Jorgensen et al [32] have used non-uniform random sampling to produce aggregate data. Hong et al have used random sampling for protecting search logs [24].…”

Section: Related Workmentioning

confidence: 99%

“…Finally, differentially private mechanisms are typically special-purpose algorithms developed for specific applications, see e.g. [17,31,32]. Many of them serve the interactive scenario, i.e.…”

Section: Introductionmentioning

confidence: 99%

SafePub: A Truthful Data Anonymization Algorithm With Strong Privacy Guarantees

Bild

Kuhn

Praßer

2018

Proceedings on Privacy Enhancing Technologies

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“…Jorgensen et al's personalized differential privacy (PDP) is a different approach to the same problem [15]. In contrast to UniTraX, PDP trusts analysts and assumes that per-user budgets are public.…”

Section: Related Workmentioning

confidence: 99%

“…ProPer would privately deduct the appropriate amount from the individual remaining budget of all users in that age range. By contrast, UniTraX publicly records that a certain amount of budget was consumed for the age range [10][11][12][13][14][15][16][17][18][19][20]. Because the consumed budget is public, the analyst can calculate how much initial budget any given point in the data parameter space would need in order to still have enough remaining budget for some specific query the analyst may wish to make.…”

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confidence: 99%

UniTraX: Protecting Data Privacy with Discoverable Biases

Munz

Eigner

Maffei

et al. 2018

Lecture Notes in Computer Science

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Abstract. An ongoing challenge with differentially private database systems is that of maximizing system utility while staying within a certain privacy budget. One approach is to maintain per-user budgets instead of a single global budget, and to silently drop users whose budget is depleted. This, however, can lead to very misleading analyses because the system cannot provide the analyst any information about which users have been dropped. This paper presents UniTraX, the first differentially private system that allows per-user budgets while providing the analyst information about the budget state. The key insight behind UniTraX is that it tracks budget not only for actual records in the system, but at all points in the domain of the database, including points that could exist but do not. UniTraX can safely report the budget state because the analyst does not know if the state refers to actual records or not. We prove that UniTraX is differentially private. UniTraX is compatible with existing differentially private analyses and our implementation on top of PINQ shows only moderate runtime overheads on a realistic workload.

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“…Jorgensen, Yu and Cormode [18] proposed a personalized differential privacy mechanism with a trustable data analyst. The algorithm of privacy protection is not performed on local clients.…”

Section: Personalized Privacymentioning

confidence: 99%

Personalized Differential Privacy Preserving Data Aggregation for Smart Homes

Zhang

Huang

Zhu

et al. 2017

Proceedings of the 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)

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Abstract-The aggregation of residents' private data drives improvements in the smart homes, however it comes with compromising on privacy. Hence, privacy preservation has become an increasing requirement for residents. Since users might have different privacy requirements, and their privacy requirements might be sensitive information, smart homes need a privacy preservation scheme to meet their demands. In this scheme, a user preserves the privacy of his/her data and privacy level locally by specifying his own privacy level in confidence, without trusting anyone else in smart homes. In addition, a user replies a single data element to the collector each time, instead of the whole dataset. It makes the scheme more challenging than the traditional centralized situation. In this paper, we propose a novel personalized local differential privacy preservation scheme for smart homes, which retains desirable utility while providing rigorous privacy guarantee.

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Conservative or liberal? Personalized differential privacy

Cited by 159 publications

References 31 publications

SafePub: A Truthful Data Anonymization Algorithm With Strong Privacy Guarantees

SafePub: A Truthful Data Anonymization Algorithm With Strong Privacy Guarantees

UniTraX: Protecting Data Privacy with Discoverable Biases

Personalized Differential Privacy Preserving Data Aggregation for Smart Homes

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