Privacy-Preserving Outlier Detection for Data Streams

Böhler, Jonas; Bernau, Daniel; Kerschbaum, Florian

doi:10.1007/978-3-319-61176-1_12

Cited by 8 publications

(12 citation statements)

References 17 publications

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“…We refer to such privacy notions as Relaxed-DP. In the context of outlier analysis, examples of Relaxed-DP are: anomaly-restricted DP [5], protected DP [15], DP over relaxed sensitivity [6], and sensitive privacy [2]. Note that if the outlier model is data-dependent (i.e.…”

Section: Privacy: the 2nd Constituent Of The Problemmentioning

confidence: 99%

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A Guide for Private Outlier Analysis

Asif

Papakonstantinou

Vaidya

2020

IEEE Lett. of the Comput. Soc.

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The increasing societal demand for data privacy has led researchers to develop methods to preserve privacy in data analysis. However, outlier analysis, a fundamental data analytics task with critical applications in medicine, finance, and national security, has only been analyzed for a few specialized cases of data privacy. This work is the first to provide a general framework for private outlier analysis, which is a two-step process. First, we show how to identify the relevant problemspecifications and then provide a practical solution that formally meets these specifications.

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Section: Privacy: the 2nd Constituent Of The Problemmentioning

confidence: 99%

“…Let f be the outlier query that to each party outputs its records (in x t ) that are among the topk outliers in x, i.e. k records with the smallest AVF score 6 . Clearly, f is an endo-query, and for x, ℒ t (f) = {i ∈ f(x) : i ∈ x t }.…”

mentioning

confidence: 99%

A Guide for Private Outlier Analysis

Asif

Papakonstantinou

Vaidya

2020

IEEE Lett. of the Comput. Soc.

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“…To address the collusion issue in [10], Random Multiparty Perturbation (RMP) technique [12] is proposed to allow each party to use a unique and different perturbation matrix to randomise their data. A recent differential privacy-based work [11] leverages a relaxed version of differential privacy to process the data in data streams. However, the differential privacy-based approaches lead to an accuracy loss in practice, while our PPOD does not degrade the accuracy comparing to the outlier detection algorithm for unencrypted datasets.…”

Section: Related Workmentioning

confidence: 99%

Enabling Efficient Privacy-Assured Outlier Detection Over Encrypted Incremental Data Sets

Lai

Yuan

Sakzad

et al. 2020

IEEE Internet Things J.

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Outlier detection is widely used in practice to track the anomaly on incremental datasets such as network traffic and system logs. However, these datasets often involve sensitive information, and sharing the data to third parties for anomaly detection raises privacy concerns. In this paper, we present a privacy-preserving outlier detection protocol (PPOD) for incremental datasets. The protocol decomposes the outlier detection algorithm into several phases and recognises the necessary cryptographic operations in each phase. It realises several cryptographic modules via efficient and interchangeable protocols to support the above cryptographic operations and composes them in the overall protocol to enable outlier detection over encrypted datasets. To support efficient updates, it integrates the sliding window model to periodically evict the expired data in order to maintain a constant update time. We build a prototype of PPOD and systematically evaluates the cryptographic modules and the overall protocols under various parameter settings. Our results show that PPOD can handle encrypted incremental datasets with a moderate computation and communication cost.

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“…Variants of the notion of differential privacy address important practical challenges. In particular, personalized differential privacy [29], protected differential privacy [31], relaxed differential privacy [6], and one-sided differential privacy [13] have a reversed order of quantification compared to sensitive privacy. Sensitive privacy, quantifies sensitive records and their privacy after quantifying the database, which is in contrast to the previous work.…”

Section: Related Workmentioning

confidence: 99%

“…This is not extensible to the case, where anomalies are defined relative to the other records [31]. Similarly, the proposed relaxed DP mechanism [6] is only applicable to anomalies defined in data-independent manner.…”

Section: Related Workmentioning

confidence: 99%

How to Accurately and Privately Identify Anomalies

Asif

Papakonstantinou

Vaidya

2019

Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security

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Identifying anomalies in data is central to the advancement of science, national security, and finance. However, privacy concerns restrict our ability to analyze data. Can we lift these restrictions and accurately identify anomalies without hurting the privacy of those who contribute their data? We address this question for the most practically relevant case, where a record is considered anomalous relative to other records. We make four contributions. First, we introduce the notion of sensitive privacy, which conceptualizes what it means to privately identify anomalies. Sensitive privacy generalizes the important concept of differential privacy and is amenable to analysis. Importantly, sensitive privacy admits algorithmic constructions that provide strong and practically meaningful privacy and utility guarantees. Second, we show that differential privacy is inherently incapable of accurately and privately identifying anomalies; in this sense, our generalization is necessary. Third, we provide a general compiler that takes as input a differentially private mechanism (which has bad utility for anomaly identification) and transforms it into a sensitively private one. This compiler, which is mostly of theoretical importance, is shown to output a mechanism whose utility greatly improves over the utility of the input mechanism. As our fourth contribution we propose mechanisms for a popular definition of anomaly ((β, r)-anomaly) that (i) are guaranteed to be sensitively private, (ii) come with provable utility guarantees, and (iii) are empirically shown to have an overwhelmingly accurate performance over a range of datasets and evaluation criteria.

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Privacy-Preserving Outlier Detection for Data Streams

Cited by 8 publications

References 17 publications

A Guide for Private Outlier Analysis

A Guide for Private Outlier Analysis

Enabling Efficient Privacy-Assured Outlier Detection Over Encrypted Incremental Data Sets

How to Accurately and Privately Identify Anomalies

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