Dare to share: Protecting sensitive knowledge with data sanitization

Amiri, Ali

doi:10.1016/j.dss.2006.08.007

Cited by 110 publications

(63 citation statements)

References 14 publications

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“…We compared the proposed approach with the "Hybrid" method in [3] and the "SIF-IDF" method in [4]. NSGA-II [9] was used as the selector of EMO.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…More methods were created based on the relation between transactions and sensitive or non-sensitive frequent itemsets. For instance, Amiri [3] proposed three heuristic algorithms which select transactions and items for sanitization in terms of sensitive itemsets and non-sensitive itemsets related. Hong et al [4] devised an itemset hiding method by using the concept of TF-IDF (Term Frequency-Inverse Document Frequency) in text mining.…”

Section: Introductionmentioning

confidence: 99%

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Manage the Tradeoff in Data Sanitization

Cheng

Lin

Pan

et al. 2015

IEICE Trans. Inf. & Syst.

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SUMMARYSharing data might bring the risk of disclosing the sensitive knowledge in it. Usually, the data owner may choose to sanitize data by modifying some items in it to hide sensitive knowledge prior to sharing. This paper focuses on protecting sensitive knowledge in the form of frequent itemsets by data sanitization. The sanitization process may result in side effects, i.e., the data distortion and the damage to the non-sensitive frequent itemsets. How to minimize these side effects is a challenging problem faced by the research community. Actually, there is a trade-off when trying to minimize both side effects simultaneously. In view of this, we propose a data sanitization method based on evolutionary multi-objective optimization (EMO). This method can hide specified sensitive itemsets completely while minimizing the accompanying side effects. Experiments on real datasets show that the proposed approach is very effective in performing the hiding task with fewer damage to the original data and non-sensitive knowledge.

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“…We compared the proposed approach with the "Hybrid" method in [3] and the "SIF-IDF" method in [4]. NSGA-II [9] was used as the selector of EMO.…”

Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Manage the Tradeoff in Data Sanitization

Cheng

Lin

Pan

et al. 2015

IEICE Trans. Inf. & Syst.

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“…The algorithm in (Oliveira and Zaiane, 2003b), called SWA, is an efficient, scalable, one-scan heuristic which aims at providing a balance between the needs for privacy and knowledge discovery in association rule hiding. Three effective, multiple association rule hiding heuristic algorithms are proposed by (Amiri, 2007) and shown that they outperform SWA by offering higher data utility and lower distortion. Five heuristic algorithms based on two strategies are proposed in (Verykios et al, 2004); first approach prevents rules from being generated, by hiding the frequent sets from which they are derived whereas the second approach reduces the importance of the rules by setting their confidence below a user-specified threshold.…”

Section: Related Workmentioning

confidence: 99%

“…Due to combinatorial nature of the problem of itemset hiding, proposed sanitization methodologies span from simple, time and memory efficient heuristics (Oliveira and Zaiane, 2002) (Oliveira and Zaiane, 2003a) (Verykios et al, 2004) (Amiri, 2007) (Wu et al, 2007) (Keer and Singh, 2012) (Yildiz and Ergenc, 2012), border-based approaches (Sun and Yu, 2005) (Sun and Yu, 2007) (Moustakides and Verykios, 2008) and reconstruction based approaches (Mielikainen, 2003) (Guo, 2007) (Lin and Liu, 2007) (Boora et al, 2009) (Mohaisen et al, 2010) to exact hiding (Menon et al, 2005) (Gkoulalas-Divanis and Verykios, 2006) (Gkoulalas-Divanis and Verykios, 2008) (Gkoulalas-Divanis andVerykios, 2009b) algorithms that offer guarantees on the quality of the computed hiding solution at an increased computational complexity cost. Whatever the technique used in sanitiza-tion; different attributes are used in selecting the transaction, itemset in the transaction or the item in the itemset to modify.…”

Section: Introductionmentioning

confidence: 99%

Full-Exact Approach for Frequent Itemset Hiding

Ergenc

Ayav

2015

International Journal of Data Warehousing and Mining

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This paper proposes a novel, exact approach that relies on integer programming for association rule hiding. A large panorama of solutions exists for the complex problem of itemset hiding: from practical heuristic approaches to more accurate exact approaches. Exact approaches provide better solutions while suffering from the lack of performance and existing exact approaches still augment their methods with heuristics to make the problem solvable. In this case, the solution may not be optimum. This work present a full-exact method, without any need for heuristics. Extensive tests are conducted on 10 real datasets to analyze distance and information loss performances of the algorithm in comparison to a former similar algorithm. Since the approach provides the optimum solution to the problem, it should be considered as a reference method.

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“…This technique involves some kind of information loss or data distortion, therefore not suitable for manufactory and management in industrial context. The data sanitisation process is discussed in [8]. In this process, a hybrid approach is recommended by Amiri.…”

Section: Introductionmentioning

confidence: 99%

Resisting Tracker Attacks by Query Terms Analysis

2010

KEM

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Abstract. Tracker attacks pose a serious threat to databases, especially those used in manufactory and management in industry. These attacks can be used to infer sensitive information in databases and they are difficult to detect. This paper proposes a new approach to dealing with such attacks by analysing each disjunctive term in every query statement. Potential tracker attacks will be detected and then suppressed to avoid any further real attacks. A sample database table and a sample attack are given and analysed to show the effectiveness of the new approach.

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Dare to share: Protecting sensitive knowledge with data sanitization

Cited by 110 publications

References 14 publications

Manage the Tradeoff in Data Sanitization

Manage the Tradeoff in Data Sanitization

Full-Exact Approach for Frequent Itemset Hiding

Resisting Tracker Attacks by Query Terms Analysis

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