Itemset Hiding under Multiple Sensitive Support Thresholds

Öztürk, Ahmet Cumhur; Bostanoglu, Belgin Ergenç

doi:10.5220/0006501502220231

Cited by 5 publications

(11 citation statements)

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“…However, extracted valuable knowledge may reveal sensitive information which seriously threatens privacy if shared without any precautions. To hide this sensitive information, Öztürk and Ergenç [74] proposed a pseudo graph based sanitization (PGBS) algorithm that focuses on concealing sensitive itemsets on a given transactional dataset. The PGBS algorithm hides the user sensitive itemsets by decreasing their supports below user specified multiple sensitive thresholds.…”

Section: B Privacy Preservationmentioning

confidence: 99%

Modern Applications and Challenges for Rare Itemset Mining

Darrab¹,

Broneske²,

Saake³

2021

IJMLC

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Data mining is the process of extracting useful unknown knowledge from large datasets. Frequent itemset mining is the fundamental task of data mining that aims at discovering interesting itemsets that frequently appear together in a dataset. However, mining infrequent (rare) itemsets may be more interesting in many real-life applications such as predicting telecommunication equipment failures, genetics, medical diagnosis, or anomaly detection. In this paper, we survey up-to-date methods of rare itemset mining. The main goal of this survey is to provide a comprehensive overview of the state-of-the-art algorithms of rare itemset mining and its applications. The main contributions of this survey can be summarized as follows. In the first part, we define the task of rare itemset mining by explaining key concepts and terminology, motivation examples, and comparisons with underlying concepts. Then, we highlight the state-of-art methods for rare itemsets mining. Furthermore, we present variations of the task of rare itemset mining to discuss limitations of traditional rare itemset mining algorithms. After that, we highlight the fundamental applications of rare itemset mining. In the last, we point out research opportunities and challenges for rare itemset mining for future research.

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Section: B Privacy Preservationmentioning

confidence: 99%

Modern Applications and Challenges for Rare Itemset Mining

Darrab¹,

Broneske²,

Saake³

2021

IJMLC

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“…It was proven that finding such an optimal solution is NP-hard problem [7]. In the literature various approaches have been proposed for the frequent itemset hiding problem including borderbased [27,34], exact [8,16,26], reconstruction based [1,9,21], cryptography based [24] and heuristic based [20,30,31]. Each approach tries to hide all sensitive itemsets while reducing the side effects given to the data and knowledge to the minimum.…”

Section: Introductionmentioning

confidence: 99%

“…In this study we propose a distortion-based heuristic frequent itemset hiding algorithm IPGBS (itemset oriented pseudo graph-based sanitization). As PGBS which is proposed with detailed benchmark in [31], the IPGBS uses pseudo graph data structure and allows database owner to assign multiple sensitive thresholds. The pseudo graph of IPGBS is more compact than that of PGBS and contains only sensitive itemsets as nodes and sensitive transaction ids as edge labels.…”

Section: Introductionmentioning

confidence: 99%

“…The main objective of IPGBS algorithm is modifying minimum number of transactions in order to cause minimum number of nonsensitive knowledge loss. The IPGBS is compared to two similar counter parts, PGBS [31] and transaction-oriented pseudo graph based sanitization (TPGBS) where the TPGBS is a version of the hiding algorithm proposed with detailed benchmark in [32]. The Pseudo graph data structure that we use in this study has two major goals.…”

Section: Introductionmentioning

confidence: 99%

“…In Section 4 proposed algorithm itemset oriented pseudo graph based sanitization (IPGBS) is introduced and explained with examples. Experimental results, comparing IPGBS with PGBS [31] and TPGBS [32] on six different databases are presented and discussed in Section 5. Finally, paper ends with conclusion remarks of Section 6.…”

Section: Introductionmentioning

confidence: 99%

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Minimizing information loss in shared data: Hiding frequent patterns with multiple sensitive support thresholds

Bostanoglu

Öztürk

2020

Statistical Analysis

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Privacy preserving data mining (PPDM) is the process of protecting sensitive knowledge from being discovered by data mining techniques in case of data sharing. Privacy preserving frequent itemset mining (PPFIM) is a subtask and NP-hard problem of PPDM. Its objective is to modify a given database in such a way that none of the sensitive itemsets of the database owner can be obtained by any frequent itemset mining technique from the modified database. The main challenge of PPFIM is to minimize the distortion given to the data and nonsensitive knowledge while sanitizing all given sensitive itemsets. Distortion-based sensitive itemset hiding algorithms decrease the support of each sensitive itemset under a predefined sensitive threshold through sanitization. Most of the distortion-based itemset hiding algorithms allow database owner to define a single sensitive threshold for each sensitive itemset. However, this is a limitation to the database owner since the importance of each sensitive itemset varies. In this paper we propose a distortion-based itemset hiding algorithm that allows database owner to assign multiple sensitive thresholds, namely itemset oriented pseudo graph based sanitization (IPGBS) algorithm. The purpose of IPGBS algorithm is to give minimum distortion to the nonsensitive knowledge and data while hiding all sensitive itemsets. For this reason, the IPGBS algorithm modifies least amount of transaction and transaction content. The performance evaluation of the IPGBS algorithm is conducted by using two different counterparts on four different databases. The results show that the IPGBS algorithm is more efficient in terms of nonsensitive frequent itemset loss on both dense and sparse databases. It has considerable good results in terms of number of transactions modified, number of items deleted, execution time and total memory allocation as well.

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Hiding sensitive itemsets without side effects

Surendra¹,

Mohan²

2018

Appl Intell

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Itemset Hiding under Multiple Sensitive Support Thresholds

Cited by 5 publications

References 0 publications

Modern Applications and Challenges for Rare Itemset Mining

Modern Applications and Challenges for Rare Itemset Mining

Minimizing information loss in shared data: Hiding frequent patterns with multiple sensitive support thresholds

Hiding sensitive itemsets without side effects

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