Differentially private sequential data publication via variable-length n-grams

Chen, Rui; Ács, Gergely; Castelluccia, Claude

doi:10.1145/2382196.2382263

Cited by 221 publications

(225 citation statements)

References 22 publications

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“…Compared with the state of the art of sequential pattern mining [6], our approach provides comparable results for frequent substring patterns, while achieving significantly superior results for mining frequent prefixes at the same time.…”

Section: Transformation and Refinementmentioning

confidence: 99%

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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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Section: Transformation and Refinementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mining frequent patterns with differential privacy

Bonomi

Xiong

2013

Proc. VLDB Endow.

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“…As examples, both compositions are usually used in building partitioning trees for data summary under differential privacy [9,17,23,24].…”

Section: Composabilitymentioning

confidence: 99%

“…To publish sequential data like Web browsing histories and mobility traces in private settings, Chen et al [24] employed a variable-length n-gram model, which is widely used in natural language processing, to extract essential information from a sequential dataset and build an exploration tree satisfying ε-DP. Synthetic data constructed from the tree can be safely used by analysts.…”

Section: ) Partitioningmentioning

confidence: 99%

Differential Privacy in Practice

Nguyen

Kim

2013

Journal of Computing Science and Engineering

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We briefly review the problem of statistical disclosure control under differential privacy model, which entails a formal and ad omnia privacy guarantee separating the utility of the database and the risk due to individual participation. It has born fruitful results over the past ten years, both in theoretical connections to other fields and in practical applications to real-life datasets. Promises of differential privacy help to relieve concerns of privacy loss, which hinder the release of community-valuable data. This paper covers main ideas behind differential privacy, its interactive versus non-interactive settings, perturbation mechanisms, and typical applications found in recent research. Category: Smart and intelligent computing

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“…Due to its nature, this approach is quite effective for prefix patterns but the results for substring patterns are quite poor. Recently, Chen et al [9] proposed an alternative way for mining sequential patterns. They first reduce the dimensionality of the pattern space by restricting the mining on short patterns (n-grams), and second use the Markov assumption to construct a sanitized dataset using the noisy patterns.…”

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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Differentially private sequential data publication via variable-length n-grams

Cited by 221 publications

References 22 publications

Mining frequent patterns with differential privacy

Mining frequent patterns with differential privacy

Differential Privacy in Practice

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

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