Lossless and robust privacy preservation of association rules in data sanitization

2018 ◽  
Vol 22 (S1) ◽  
pp. 1415-1428 ◽  
Author(s):  
Geeta S. Navale ◽  
Suresh N. Mali
Keyword(s):  
Association Rules ◽  
Privacy Preservation ◽  
Data Sanitization

Self-Adaptive Optimization for Improved Data Sanitization and Restoration

2020 ◽  
Vol 28 (03) ◽  
pp. 391-420
Author(s):  
Geeta S. Navale ◽  
Suresh N. Mali
Keyword(s):  
Data Mining ◽  
Privacy Preservation ◽  
Loss Rate ◽  
Differential Evolution Algorithm ◽  
Adaptive Optimization ◽  
Data Set ◽  
Data Sanitization ◽  
Data Restoration ◽  
Evolution Algorithm ◽  
Self Adaptive

Nowadays, Data Sanitization is considered as a highly demanded area for solving the issue of privacy preservation in Data mining. Data Sanitization, means that the sensitive rules given by the users with the specific modifications and then releases the modified database so that, the unauthorized users cannot access the sensitive rules. Promisingly, the confidentiality of data is ensured against the data mining methods. The ultimate goal of this paper is to build an effective sanitization algorithm for hiding the sensitive rules given by users/experts. Meanwhile, this paper concentrates on minimizing the four sanitization research challenges namely, rate of hiding failure, rate of Information loss, rate of false rule generation and degree of modification. Moreover, this paper proposes a heuristic optimization algorithm named Self-Adaptive Firefly (SAFF) algorithm to generate the small length key for data sanitization and also to adopt lossless data sanitization and restoration. The generated optimized key is used for both data sanitation as well as the data restoration process. The proposed SAFF-based algorithm is compared and examined against the other existing sanitizing algorithms like Fire Fly (FF), Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution algorithm (DE) algorithms and the results have shown the excellent performance of proposed algorithm. The proposed algorithm is implemented in JAVA. The data set used are Chess, Retail, T10, and T40.

scholarly journals Algorithmic Analysis on Adaptive Dragonfly based Optimal Key Generation for Privacy Preservation in IoT

2020 ◽  
Vol 9 (4) ◽  
pp. 915-922
Keyword(s):  
Privacy Preservation ◽  
The Internet ◽  
Key Generation ◽  
Dragonfly Algorithm ◽  
Data Sanitization ◽  
Algorithmic Analysis ◽  
Medical Healthcare ◽  
The Internet Of Things ◽  
Exchange Data ◽  
Selection Of

The Internet of Things (IoT) is a network of Internet-enabled devices that can sense, communicate, and react to changes in their environment. It is commonly applied in many applications, like building automation, medical healthcare systems, transportation, environment monitoring, and energy management. Billions of these computing devices are connected to the Internet to exchange data between themselves and/or their infrastructure. However, the privacy of data seems to be the greatest issue that needs to be solved. This paper intends to develop an improved data sanitization and restoration framework in IoT for higher-order privacy preservation. The preservation process is carried out using key that is optimally selected. For the optimal selection of key, a new Improved Dragonfly Algorithm (IDA) is introduced. Finally, the algorithmic analysis is carried out by varying parameters like enemy distraction weight e and food attraction weight f  of the proposed algorithm

scholarly journals Survey on Association Rule Hiding Techniques

2019 ◽  
pp. 300-305
Author(s):  
G. Bhavani ◽  
S. Sivakumari
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Association Rules ◽  
Association Rule ◽  
Privacy Preservation ◽  
Sensitive Information ◽  
Privacy Preserving Data Mining ◽  
Interesting Part ◽  
Future Improvement ◽  
Sensitive Knowledge

Data mining process extracts useful information from a large amount of data. The most interesting part of data mining is discovering the unseen patterns without unpacking sensitive knowledge. Privacy Preserving Data Mining abbreviated as PPDM deals with the issue of sustaining the privacy of information. This methodology covers the sensitive information from disclosure. PPDM techniques are established for hiding the sensitive information even after performing the data mining. One of the practices to hide the sensitive association rules is termed as association rule hiding. The main objective of association rule hiding algorithm is to slightly adjust the original database so that no sensitive association rule is derived from it. The following article presents a detailed survey of various association rule hiding techniques for preserving privacy in data mining. At first, different techniques developed by previous researchers are studied in detail. Then, a comparative analysis is carried out to know the limitations of each technique and then providing a suggestion for future improvement in association rule hiding for privacy preservation.

scholarly journals Association Rule Hiding Based on Intersection Lattice

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Hai Quoc Le ◽  
Somjit Arch-int ◽  
Ngamnij Arch-int
Keyword(s):  
Side Effects ◽  
Association Rules ◽  
Association Rule ◽  
Low Complexity ◽  
Vital Role ◽  
Frequent Itemsets ◽  
Intersection Lattice ◽  
Data Sanitization ◽  
Knowledge Preservation ◽  
Sensitive Knowledge

Association rule hiding has been playing a vital role in sensitive knowledge preservation when sharing data between enterprises. The aim of association rule hiding is to remove sensitive association rules from the released database such that side effects are reduced as low as possible. This research proposes an efficient algorithm for hiding a specified set of sensitive association rules based on intersection lattice of frequent itemsets. In this research, we begin by analyzing the theory of the intersection lattice of frequent itemsets and the applicability of this theory into association rule hiding problem. We then formulate two heuristics in order to (a) specify the victim items based on the characteristics of the intersection lattice of frequent itemsets and (b) identify transactions for data sanitization based on the weight of transactions. Next, we propose a new algorithm for hiding a specific set of sensitive association rules with minimum side effects and low complexity. Finally, experiments were carried out to clarify the efficiency of the proposed approach. Our results showed that the proposed algorithm, AARHIL, achieved minimum side effects and CPU-Time when compared to current similar state of the art approaches in the context of hiding a specified set of sensitive association rules.

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