On differentially private frequent itemset mining

Zeng, Chen; Naughton, Jeffrey F.; Cai, Jin-Yi

doi:10.14778/2428536.2428539

Cited by 88 publications

(116 citation statements)

References 22 publications

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“…Roth and Roughgarden [30] propose the median mechanism for an interactive setting, where the adversary requests statistics about D multiple times in an adaptive fashion. There are also works that focus on other specialized scenarios, such as range-count queries [32,8], query consistency [19], sparse data [9,23], correlated queries [18,21], frequent itemsets [33,22], trajectories [20], minimization of relative error [31], and non-numerical query answers [26]. PINQ [25] is a system implementation that integrates differential privacy with data analysis.…”

Section: Differential Privacymentioning

confidence: 99%

Differentially private event sequences over infinite streams

Kellaris¹,

et al. 2014

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Numerous applications require continuous publication of statistics for monitoring purposes, such as real-time traffic analysis, timely disease outbreak discovery, and social trends observation. These statistics may be derived from sensitive user data and, hence, necessitate privacy preservation. A notable paradigm for offering strong privacy guarantees in statistics publishing is ϵ-differential privacy. However, there is limited literature that adapts this concept to settings where the statistics are computed over an infinite stream of "events" (i.e., data items generated by the users), and published periodically. These works aim at hiding a single event over the entire stream. We argue that, in most practical scenarios, sensitive information is revealed from multiple events occurring at contiguous time instances. Towards this end, we put forth the novel notion of w-event privacy over infinite streams, which protects any event sequence occurring in w successive time instants. We first formulate our privacy concept, motivate its importance, and introduce a methodology for achieving it. We next design two instantiations, whose utility is independent of the stream length. Finally, we confirm the practicality of our solutions experimenting with real data.

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Section: Differential Privacymentioning

confidence: 99%

Differentially private event sequences over infinite streams

Kellaris¹,

et al. 2014

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“…In the data mining community, the pattern mining problem is often associated with patterns in the form of itemsets. Despite the wide range of algorithmic solutions to this problem, only a few approaches [4,14,24] study the mining of frequent patterns with differential privacy. However, due to the nature of the patterns, these approaches are not suitable in our setting.…”

Section: Related Workmentioning

confidence: 99%

“…This privacy framework provides strong and provable guarantees of privacy and it has become the de facto standard for research in data privacy. Only recently, several techniques [4,14,24] have been proposed for mining frequent patterns under this privacy model. Although these solutions have been shown to be effective in some scenarios, the pattern model used is in the form of itemset which makes these approaches unsuitable for capturing the sequentiality of the events in the data.…”

Section: Introductionmentioning

confidence: 99%

A two-phase algorithm for mining sequential patterns with differential privacy

Bonomi

Xiong

2013

Proceedings of the 22nd ACM International Conference on Conference on Information &Amp; Knowledge Management - CIKM '13

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Frequent sequential pattern mining is a central task in many fields such as biology and finance. However, release of these patterns is raising increasing concerns on individual privacy. In this paper, we study the sequential pattern mining problem under the differential privacy framework which provides formal and provable guarantees of privacy. Due to the nature of the differential privacy mechanism which perturbs the frequency results with noise, and the high dimensionality of the pattern space, this mining problem is particularly challenging. In this work, we propose a novel two-phase algorithm for mining both prefixes and substring patterns. In the first phase, our approach takes advantage of the statistical properties of the data to construct a model-based prefix tree which is used to mine prefixes and a candidate set of substring patterns. The frequency of the substring patterns is further refined in the successive phase where we employ a novel transformation of the original data to reduce the perturbation noise. Extensive experiment results using real datasets showed that our approach is effective for mining both substring and prefix patterns in comparison to the state-of-theart solutions.

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“…The PrivBasis approach proposed in [16] introduces a new mining technique based on the concept of basis set which allows to efficiently and effectively construct the set of frequent itemsets from a small set of short patterns. Recently, Zeng et al [25] pointed out the hardness of the differentially private frequent itemsets mining problem by investigating the trade-off between privacy and utility. As a negative result the authors showed that in order to achieve certain level of utility and privacy guarantee we could incur an extremely high privacy cost due to the presence of long transactions that increase the sensitivity of the count query for the itemsets.…”

Section: Existing Work For Frequent Itemset Miningmentioning

confidence: 99%

“…In this setting, only few works have been proposed to mine frequent patterns [3,16,25]. Although these techniques have been shown effective in certain scenarios, they have the following limitations.…”

Section: Introductionmentioning

confidence: 99%

Mining frequent patterns with differential privacy

Bonomi

Xiong

2013

Proc. VLDB Endow.

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The mining of frequent patterns is a fundamental component in many data mining tasks. A considerable amount of research on this problem has led to a wide series of efficient and scalable algorithms for mining frequent patterns. However, releasing these patterns is posing concerns on the privacy of the users participating in the data. Indeed the information from the patterns can be linked with a large amount of data available from other sources creating opportunities for adversaries to break the individual privacy of the users and disclose sensitive information. In this proposal, we study the mining of frequent patterns in a privacy preserving setting. We first investigate the difference between sequential and itemset patterns, and second we extend the definition of patterns by considering the absence and presence of noise in the data. This leads us in distinguishing the patterns between exact and noisy. For exact patterns, we describe two novel mining techniques that we previously developed. The first approach has been applied in a privacy preserving record linkage setting, where our solution is used to mine frequent patterns which are employed in a secure transformation procedure to link records that are similar. The second approach improves the mining utility results using a two-phase strategy which allows to effectively mine frequent substrings as well as prefixes patterns. For noisy patterns, first we formally define the patterns according to the type of noise and second we provide a set of potential applications that require the mining of these patterns. We conclude the paper by stating the challenges in this new setting and possible future research directions.

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On differentially private frequent itemset mining

Cited by 88 publications

References 22 publications

Differentially private event sequences over infinite streams

Differentially private event sequences over infinite streams

A two-phase algorithm for mining sequential patterns with differential privacy

Mining frequent patterns with differential privacy

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