Comprehensive Survey on Privacy Preserving Association Rule Mining: Models, Approaches, Techniques and Algorithms

Alwatban, Ibrahim S.; Emam, Ahmed

doi:10.1142/s0218213014500043

Cited by 10 publications

(3 citation statements)

References 46 publications

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“…Early research on data sanitisation discusses distributed transactional databases and addresses the problem of maximising the utilisation of data with the use of sensitive association rules where sensitive and private data are being redacted [3]. In the relevant literature, various privacy preserving models are presented towards data and/or knowledge protection through sanitisation [4], [5]. The existence of multiple sanitisation solutions (and the lack of commonly agreed practises can be considered another added weight to the fact that there is not a common solution for sharing the cyber threat data yet [6].…”

Section: Related Workmentioning

confidence: 99%

Data Sanitisation and Redaction for Cyber Threat Intelligence Sharing Platforms

Yücel

Chalkias

Mallis

et al. 2021

2021 IEEE International Conference on Cyber Security and Resilience (CSR)

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The recent technological advances and the recent changes in the daily human activities increased the production and sharing of data. In the ecosystem of interconnected systems, data can be circulated among systems for various reasons. This could lead to exchange of private or sensitive information between entities. Data Sanitisation involves processes and practices that remove sensitive and private information from documents before sharing them with entities that should not be exposed to the removed information. This paper presents the design and development of a data sanitisation and redaction solution for a Cyber Threat Intelligence sharing platform. The Data Sanitisation and Redaction Plugin has been designed with the purpose of operating as a plugin for the ECHO Project's Early Warning System platform and enhancing its operative capabilities during information sharing. This plugin aims to provide automated security and privacy-based controls to the concept of CTI sharing over a ticketing system. The plugin has been successfully tested and the results are presented in this paper.

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Section: Related Workmentioning

confidence: 99%

Data Sanitisation and Redaction for Cyber Threat Intelligence Sharing Platforms

Yücel

Chalkias

Mallis

et al. 2021

2021 IEEE International Conference on Cyber Security and Resilience (CSR)

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“…In case of vertically partitioned data, each participant has different schema and it stores the data of the same set of entities. Privacy-preserving association rule mining on vertically partitioned data is discussed in [26][27][28][29][30][31]. As shown in figure 3, the hospital EHR system stores the data of patients and the cell-phone company stores the call details of these patients based on common ID.…”

Section: Data Partition Modelmentioning

confidence: 99%

Privacy-preserving association rule mining for horizontally partitioned healthcare data: a case study on the heart diseases

Domadiya

Rao

2018

Sādhanā

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In recent years, a trend of electronic health record (EHR) system can be seen increasingly in the hospitals, which has generated huge amount of electronically stored data of patients. Association rule mining technique is very helpful in the numerous applications of healthcare (e.g., correlation between disease and symptoms, disease and offering effective treatment and predicting risks of disease based on the historical data, etc.). The data collected by an EHR system are very important for the medical research. Currently, a patient health report is derived on the basis of a physician's own experience and on the association rule mining results of a local EHR system maintained by a particular hospital. Association rule mining results will be more accurate if the data of all local EHR systems are integrated and association rule mining is performed. Integration of local EHR systems requires the sharing of local EHR data. Sharing of patient records violates the privacy of patients. Hence, medical research is focused on the problem of mining association rules without sharing of local private EHR data. Privacy-preserving distributed association rule mining (PPDARM) solves this issue by mining the association rules while preserving the privacy of patients. In this paper, an approach for the PPDARM is proposed for collaboratively performing association rule mining by all local EHR systems while preserving the privacy. The proposed approach is also analysed with the heart disease dataset.

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“…Most of the PPARM reviews concentrate on the classification and summary of the techniques [3]- [9]. In [3], [4], the authors conducted a comprehensive survey on algorithms, techniques of association rule hiding. In [5]- [7], the authors took a survey of privacy preserving techniques in ARM over distributed data.…”

Section: Introductionmentioning

confidence: 99%

Privacy Preserving Association Rule Mining: Taxonomy, Techniques, and Metrics

2019

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In the last decades, pervasive computing is generating a growing quantity of data. Data mining (DM) technology has become increasingly popular. However, the excessive collection and analysis of data may violate the privacy of individuals and organizations, which raises privacy concern. Therefore, a new research area known as privacy-preserving DM (PPDM) has emerged and attracted the attention of many researchers who are interested in preventing privacy disclosure during DM. In this paper, we provide a comprehensive review of studies on a specific PPDM, known as privacy-preserving association rule mining (PPARM). We present a detailed taxonomy for the existing PPARM algorithms according to multiple dimensions and then conduct a survey of the most relevant PPARM techniques from the literature. Moreover, we survey and elaborate on each type of metrics used to evaluate PPARM algorithms. Finally, we summarize some conclusions and come up with some future directions and challenges. INDEX TERMS Data mining, privacy preserving, association rule mining, association rule hiding, frequent itemsets, privacy metrics, data utility metrics, complexity metrics.

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Comprehensive Survey on Privacy Preserving Association Rule Mining: Models, Approaches, Techniques and Algorithms

Cited by 10 publications

References 46 publications

Data Sanitisation and Redaction for Cyber Threat Intelligence Sharing Platforms

Data Sanitisation and Redaction for Cyber Threat Intelligence Sharing Platforms

Privacy-preserving association rule mining for horizontally partitioned healthcare data: a case study on the heart diseases

Privacy Preserving Association Rule Mining: Taxonomy, Techniques, and Metrics

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