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.
Data sanitization ( knowledge hiding) aims at concealing patterns modeling confidential knowledge by limiting their frequency, so that they are not easily mined from the data. Existing methods are applied to: (I) a of set-valued data (transactions) @cite_4 or spatiotemporal data (trajectories) @cite_16 ; (II) a of sequences @cite_11 @cite_0 ; or (III) a sequence @cite_5 @cite_2 @cite_12 . Yet, none of these methods follows our CSD setting: Methods in category I are not applicable to string data, and those in categories II and III do not have guarantees on privacy-related constraints @cite_12 or on utility-related properties @cite_11 @cite_0 @cite_5 @cite_2 . Specifically, unlike our approach, @cite_12 cannot guarantee that all sensitive patterns are concealed (constraint C1 ), while @cite_11 @cite_0 @cite_5 @cite_2 do not guarantee the satisfaction of utility properties ( @math and P2 ).
@cite_4 @cite_0 @cite_2 @cite_5 @cite_16 @cite_12 @cite_11
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Sequence datasets are encountered in a plethora of applications spanning from web usage analysis to healthcare studies and ubiquitous computing. Disseminating such datasets offers remarkable opportunities for discovering interesting knowledge patterns, but may lead to serious privacy violations if sensitive patterns, such as business secrets, are disclosed. In this work, we consider how to sanitize data to prevent the disclosure of sensitive patterns during sequential pattern mining, while ensuring that the nonsensitive patterns can still be discovered. First, we re-define the problem of sequential pattern hiding to capture the information loss incurred by sanitization in terms of both events' modification (distortion) and lost nonsensitive knowledge patterns (side-effects). Second, we model sequences as graphs and propose two algorithms to solve the problem by operating on the graphs. The first algorithm attempts to sanitize data with minimal distortion, whereas the second focuses on reducing the side-effects. Extensive experiments show that our algorithms outperform the existing solution in terms of data distortion and side-effects and are more efficient. We propose a methodology for hiding all sensitive frequent itemsets in a transaction database. Our methodology relies on a novel technique that enumerates the minimal transversals of a hypergraph in order to induce the ideal border between frequent and sensitive itemsets. The ideal border is then utilized to formulate an integer linear program (ILP) that answers whether a feasible sanitized database that attains the ideal border, exists. The solution of the program identifies the set of transactions that need to be modified (sanitized) so that the hiding can be achieved with the maximum accuracy. If no solution exists, we modify the ILP by relaxing the constraints needed to be satisfied so that the sanitized database preserves the privacy with guarantee but with minimum effect in data quality. Experimental evaluation of the proposed approach on a number of real datasets has shown that the produced sanitized databases exhibit higher accuracy when compared with the solutions of other well-known approaches. The process of discovering relevant patterns holding in a database was first indicated as a threat to database security by O'Leary in. Since then, many different approaches for knowledge hiding have emerged over the years, mainly in the context of association rules and frequent item sets mining. Following many real-world data and application demands, in this paper, we shift the problem of knowledge hiding to contexts where both the data and the extracted knowledge have a sequential structure. We define the problem of hiding sequential patterns and show its NP-hardness. Thus, we devise heuristics and a polynomial sanitization algorithm. Starting from this framework, we specialize it to the more complex case of spatiotemporal patterns extracted from moving objects databases. Finally, we discuss a possible kind of attack to our model, which exploits the knowledge of the underlying road network, and enhance our model to protect from this kind of attack. An exhaustive experiential analysis on real-world data sets shows the effectiveness of our proposal. Nowadays, sharing data among organizations is often required during the business collaboration. Data mining technology has enabled efficient extraction of knowledge from large databases. This, however, increases risks of disclosing the sensitive knowledge when the database is released to other parties. To address this privacy issue, one may sanitize the original database so that the sensitive knowledge is hidden. The challenge is to minimize the side effect on the quality of the sanitized database so that non-sensitive knowledge can still be mined. In this paper, we study such a problem in the context of hiding sensitive frequent itemsets by judiciously modifying the transactions in the database. Unlike previous work, we consider the quality of the sanitized database especially on preserving the non-sensitive frequent itemsets. To preserve the non-sensitive frequent itemsets, we propose a border-based approach to efficiently evaluate the impact of any modification to the database during the hiding process. The quality of database can be well maintained by greedily selecting the modifications with minimal side effect. Experiments results are also reported to show the effectiveness of the proposed approach.