Dare to share: Protecting sensitive knowledge with data sanitization

2007 ◽  
Vol 43 (1) ◽  
pp. 181-191 ◽  
Author(s):  
Ali Amiri
Keyword(s):  
Data Sanitization ◽  
Sensitive Knowledge

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.

Impact factor based data sanitization in association rule mining

2021 ◽  
Author(s):  
S. Nithya ◽  
M. Sangeetha ◽  
K.N. Apinaya Prethi ◽  
S. Vellingiri
Keyword(s):  
Impact Factor ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Data Sanitization

Data sanitization against adversarial label contamination based on data complexity

2017 ◽  
Vol 9 (6) ◽  
pp. 1039-1052 ◽  
Author(s):  
Patrick P. K. Chan ◽  
Zhi-Min He ◽  
Hongjiang Li ◽  
Chien-Chang Hsu
Keyword(s):  
Data Complexity ◽  
Data Sanitization

scholarly journals Data Sanitization Framework for Computer Hard Disk Drive: A Case Study in Malaysia

2019 ◽  
Vol 10 (11) ◽  
Author(s):  
Nooreen Ashilla Binti Yusof ◽  
Siti Norul ◽  
Mohamad Firham ◽  
Nor Zarina ◽  
Monaliza Binti
Keyword(s):  
Hard Disk Drive ◽  
Hard Disk ◽  
Disk Drive ◽  
Data Sanitization

Privacy Preserving Fuzzy Association Rule Mining in Data Clusters Using Particle Swarm Optimization

2021 ◽  
pp. 1096-1116
Author(s):  
Sathiyapriya Krishnamoorthy ◽  
G. Sudha Sadasivam ◽  
M. Rajalakshmi ◽  
K. Kowsalyaa ◽  
M. Dhivya
Keyword(s):  
Particle Swarm Optimization ◽  
Side Effects ◽  
Association Rule ◽  
Association Rule Mining ◽  
Particle Swarm ◽  
Privacy Preserving ◽  
Rule Mining ◽  
Swarm Optimization ◽  
Fuzzy Association Rule ◽  
Sensitive Knowledge

An association rule is classified as sensitive if its thread of revelation is above certain confidence value. If these sensitive rules were revealed to the public, it is possible to deduce sensitive knowledge from the published data and offers benefit for the business competitors. Earlier studies in privacy preserving association rule mining focus on binary data and has more side effects. But in practical applications the transactions contain the purchased quantities of the items. Hence preserving privacy of quantitative data is essential. The main goal of the proposed system is to hide a group of interesting patterns which contains sensitive knowledge such that modifications have minimum side effects like lost rules, ghost rules, and number of modifications. The proposed system applies Particle Swarm Optimization to a few clusters of particles thus reducing the number of modification. Experimental results demonstrate that the proposed approach is efficient in terms of lost rules, number of modifications, hiding failure with complete avoidance of ghost rules.

Perceptual knowledge

2019 ◽  
pp. 279-308
Author(s):  
Georges Dicker
Keyword(s):  
Perceptual Experience ◽  
Perceptual Knowledge ◽  
The Senses ◽  
Carry Over ◽  
Sensitive Knowledge ◽  
Rule Out

This chapter critically analyzes Locke’s views on “sensitive knowledge.” Its main theses are: (1) Locke sometimes confuses the legitimate question (Q1), “When we perceive a body, how can we know that we aren’t hallucinating instead?” with the faulty “veil-of-perception” question, (Q2) “How do we know bodies exist, since we can’t perceive them?” (2) When Locke does mention (Q1), he sometimes just dismisses it, because he holds that simple ideas of sensation are by definition produced by bodies. (3) At other times, Locke humors the skeptic, and offers a defense of the senses, in the form of an inference to the best explanation. (4) It’s doubtful that he could successfully rule out other possible explanations of our perceptual experience, like Descartes’s deceiver scenario and its contemporary variants. (5) There are reasons for this weakness, and they carry over to any attempt to defeat skepticism by an inference to the best explanation.

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