Permutation-Based Sequential Pattern Hiding

Gwadera, Robert; Gkoulalas-Divanis, Aris; Loukides, Grigorios

doi:10.1109/icdm.2013.57

Cited by 12 publications

(32 citation statements)

References 14 publications

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“…Thus, fewer ghosts imply that data utility is higher. The notion of ghost events appears in [14] but here we adapt it to event sequences.…”

Section: Methodsmentioning

confidence: 99%

“…Note that we treat sensitive events as not exposed, when their relative frequency is lower than δ. This approach aims to minimize the impact of deletion on data utility and is adopted by most sanitization methods [23,3,11,14,24,19]. It makes the reasonable assumption that sensitive knowledge is not actionable, when it is mined at a lower threshold than δ.…”

Section: Problem (Event Sequence Sanitization (Ess))mentioning

confidence: 99%

“…Existing sanitization approaches are applied to a collection (multiset) of transactions [23,24,19], sequences [3,11,14], or trajectories [3]. Most approaches prevent the mining of frequent sensitive patterns [23,3,11] or association rules [24,19], by applying deletion.…”

Section: Related Workmentioning

confidence: 99%

“…Most approaches prevent the mining of frequent sensitive patterns [23,3,11] or association rules [24,19], by applying deletion. To preserve utility, these approaches attempt to minimize the total number of deleted items (events) [11] and/or changes in the frequency of nonsensitive patterns [3], as well as in the output of frequent pattern [23,11,14] or association rule [24,19] mining. On the contrary, we consider a long sequence of events that are associated with time points and follow differing probability distributions, in different parts of the sequence.…”

Section: Related Workmentioning

confidence: 99%

“…These events represent confidential knowledge, such as business secrets, sensitive religious beliefs of groups of individuals, or health-related information of patient subpopulations that may cause unwarranted concerns (e.g., flu-associated hospitalizations). Thus, data owners must ensure that sensitive events are not discovered, when frequent event mining is applied using a specified threshold [24,23,19,3,11,14]. As a motivation, consider the following example.…”

Section: Introductionmentioning

confidence: 99%

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Optimal event sequence sanitization

Loukides¹,

Gwadera²

2015

Proceedings of the 2015 SIAM International Conference on Data Mining

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Frequent event mining is a fundamental task to extract insight from an event sequence (long sequence of events that are associated with time points). However, it may expose sensitive events that leak confidential business knowledge or lead to intrusive inferences about groups of individuals. In this work, we aim to prevent this threat, by deleting occurrences of sensitive events, while preserving the utility of the event sequence. To quantify utility, we propose a model that captures changes, caused by deletion, to the probability distribution of events across the sequence. Based on the model, we define the problem of sanitizing an event sequence as an optimization problem. Solving the problem is important to preserve the output of many mining tasks, including frequent pattern mining and sequence segmentation. However, this is also challenging, due to the exponential number of ways to apply deletion to the sequence. To optimally solve the problem when there is one sensitive event, we develop an efficient algorithm based on dynamic programming. The algorithm also forms the basis of a simple, iterative method that optimally sanitizes an event sequence, when there are multiple sensitive events. Experiments on real and synthetic datasets show the effectiveness and efficiency of our method.

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“…Thus, fewer ghosts imply that data utility is higher. The notion of ghost events appears in [14] but here we adapt it to event sequences.…”

Section: Methodsmentioning

confidence: 99%

Section: Problem (Event Sequence Sanitization (Ess))mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimal event sequence sanitization

Loukides¹,

Gwadera²

2015

Proceedings of the 2015 SIAM International Conference on Data Mining

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String Sanitization: A Combinatorial Approach

Bernardini

Chen

Conte

et al. 2020

Machine Learning and Knowledge Discovery in Databases

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String data are often disseminated to support applications such as location-based service provision or DNA sequence analysis. This dissemination, however, may expose sensitive patterns that model confidential knowledge (e.g., trips to mental health clinics from a string representing a user's location history). In this paper, we consider the problem of sanitizing a string by concealing the occurrences of sensitive patterns, while maintaining data utility. First, we propose a time-optimal algorithm, TFS-ALGO, to construct the shortest string preserving the order of appearance and the frequency of all non-sensitive patterns. Such a string allows accurately performing tasks based on the sequential nature and pattern frequencies of the string. Second, we propose a time-optimal algorithm, PFS-ALGO, which preserves a partial order of appearance of non-sensitive patterns but produces a much shorter string that can be analyzed more efficiently. The strings produced by either of these algorithms may reveal the location of sensitive patterns. In response, we propose a heuristic, MCSR-ALGO, which replaces letters in these strings with carefully selected letters, so that sensitive patterns are not reinstated and occurrences of spurious patterns are prevented. We implemented our sanitization approach that applies TFS-ALGO, PFS-ALGO and then MCSR-ALGO and experimentally show that it is effective and efficient.

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A Survey of Anonymization Algorithms for Electronic Health Records

Gkoulalas-Divanis

Loukides

2015

Medical Data Privacy Handbook

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Permutation-Based Sequential Pattern Hiding

Cited by 12 publications

References 14 publications

Optimal event sequence sanitization

Optimal event sequence sanitization

String Sanitization: A Combinatorial Approach

A Survey of Anonymization Algorithms for Electronic Health Records

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