Privacy preserving frequent itemset mining: Maximizing data utility based on database reconstruction

Li, Shaoxin; Mu, Nankun; Le, Junqing; Liao, Xiaofeng

doi:10.1016/j.cose.2019.03.008

Cited by 29 publications

(9 citation statements)

References 25 publications

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“…For the reconstruction-based method, the dataset is reconstructed by a certain method and then released to the public [20][21][22] . By database reconstruction-based technologies, a data publisher can release the dataset for privacy-preserving mining association rules.…”

Section: Perturbation-based Data Publicationmentioning

confidence: 99%

“…By database reconstruction-based technologies, a data publisher can release the dataset for privacy-preserving mining association rules. The reason lies that a published dataset can be partially or fully synthetic based on the requirements of hiding SARs while guaranteeing the utility of the dataset [22] . The kind of methods are interesting and are worth investigating for further researches about the minable data publication.…”

Section: Perturbation-based Data Publicationmentioning

confidence: 99%

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An Optimized Sanitization Approach for Minable Data Publication

Yang

Liao

2022

Big Data Min. Anal.

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Minable data publication is ubiquitous since it is beneficial to sharing/trading data among commercial companies and further facilitates the development of data-driven tasks. Unfortunately, the minable data publication is often implemented by publishers with limited privacy concerns such that the published dataset is minable by malicious entities. It prohibits minable data publication since the published data may contain sensitive information. Thus, it is urgently demanded to present some approaches and technologies for reducing the privacy leakage risks.To this end, in this paper, we propose an optimized sanitization approach for minable data publication (named as SA-MDP). SA-MDP supports association rules mining function while providing privacy protection for specific rules.In SA-MDP, we consider the trade-off between the data utility and the data privacy in the minable data publication problem. To address this problem, SA-MDP designs a customized particle swarm optimization (PSO) algorithm, where the optimization objective is determined by both the data utility and the data privacy. Specifically, we take advantage of PSO to produce new particles, which is achieved by random mutation or learning from the best particle.Hence, SA-MDP can avoid the solutions being trapped into local optima. Besides, we design a proper fitness function to guide the particles to run towards the optimal solution. Additionally, we present a preprocessing method before the evolution process of the customized PSO algorithm to improve the convergence rate. Finally, the proposed SA-MDP approach is performed and verified over several datasets. The experimental results have demonstrated the effectiveness and efficiency of SA-MDP.

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Section: Perturbation-based Data Publicationmentioning

confidence: 99%

Section: Perturbation-based Data Publicationmentioning

confidence: 99%

An Optimized Sanitization Approach for Minable Data Publication

Yang

Liao

2022

Big Data Min. Anal.

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“…Frequency estimation is a classic use case for privacy protection since analytics translate observations into the frequency of relevant attributes. Such statistical translation helps find relevant behavior such as the heavy hitters (Ben Basat et al 2020;Pekar et al 2021;Wang et al 2021b;Zhao et al 2022), frequent items (Luna et al 2019;Wang et al 2018a;Djenouri et al 2018Djenouri et al , 2019Rouane et al 2019;Li et al 2019), or finding the marginals (Zhang et al 2018;Cormode et al 2018;Xue et al 2021;Wang et al 2019a, b, c). While individuals who comprise the records require plausible deniability from participation in the record, the statistical values should not deviate to extremes.…”

Section: Literature Surveymentioning

confidence: 99%

Sarve: synthetic data and local differential privacy for private frequency estimation

Varma

Chauhan

Singh³

2022

Cybersecurity

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The collection of user attributes by service providers is a double-edged sword. They are instrumental in driving statistical analysis to train more accurate predictive models like recommenders. The analysis of the collected user data includes frequency estimation for categorical attributes. Nonetheless, the users deserve privacy guarantees against inadvertent identity disclosures. Therefore algorithms called frequency oracles were developed to randomize or perturb user attributes and estimate the frequencies of their values. We propose Sarve, a frequency oracle that used Randomized Aggregatable Privacy-Preserving Ordinal Response (RAPPOR) and Hadamard Response (HR) for randomization in combination with fake data. The design of a service-oriented architecture must consider two types of complexities, namely computational and communication. The functions of such systems aim to minimize the two complexities and therefore, the choice of privacy-enhancing methods must be a calculated decision. The variant of RAPPOR we had used was realized through bloom filters. A bloom filter is a memory-efficient data structure that offers time complexity of O(1). On the other hand, HR has been proven to give the best communication costs of the order of log(b) for b-bits communication. Therefore, Sarve is a step towards frequency oracles that exhibit how privacy provisions of existing methods can be combined with those of fake data to achieve statistical results comparable to the original data. Sarve also implemented an adaptive solution enhanced from the work of Arcolezi et al. The use of RAPPOR was found to provide better privacy-utility tradeoffs for specific privacy budgets in both high and general privacy regimes.

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“…Pang et al [15] devised a sensitive association rule hiding algorithm on outsourced data uploaded from multiple data owners in a twin cloud architecture using homomorphic cryptosystem. Shaoxin et al [16] proposed a database reconstruction-based technique for hiding frequent itemsets achieves a high degree of privacy and reasonable data utility of the synthetic database. The main drawback of the heuristic approach is that in the majority of cases, it fails to deliver an optimal solution to the sanitization problem.…”

Section: Related Workmentioning

confidence: 99%

Association rule hiding using integer linear programming

Suma

Shobha

2021

IJECE

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<span>Privacy preserving data mining has become the focus of attention of government statistical agencies and database security research community who are concerned with preventing privacy disclosure during data mining. Repositories of large datasets include sensitive rules that need to be concealed from unauthorized access. Hence, association rule hiding emerged as one of the powerful techniques for hiding sensitive knowledge that exists in data before it is published. In this paper, we present a constraint-based optimization approach for hiding a set of sensitive association rules, using a well-structured integer linear program formulation. The proposed approach reduces the database sanitization problem to an instance of the integer linear programming problem. The solution of the integer linear program determines the transactions that need to be sanitized in order to conceal the sensitive rules while minimizing the impact of sanitization on the non-sensitive rules. We also present a heuristic sanitization algorithm that performs hiding by reducing the support or the confidence of the sensitive rules. The results of the experimental evaluation of the proposed approach on real-life datasets indicate the promising performance of the approach in terms of side effects on the original database.</span>

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Privacy preserving frequent itemset mining: Maximizing data utility based on database reconstruction

Cited by 29 publications

References 25 publications

An Optimized Sanitization Approach for Minable Data Publication

An Optimized Sanitization Approach for Minable Data Publication

Sarve: synthetic data and local differential privacy for private frequency estimation

Association rule hiding using integer linear programming

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