Probabilistic Grid-Based Approaches for Privacy-Preserving Data Mining on Moving Object Trajectories

2010 ◽  
pp. 183-210 ◽  
Author(s):  
Gyozo Gidofalvi ◽  
Xuegang Huang ◽  
Torben Bach Pedersen
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Moving Object ◽  
Privacy Preserving Data Mining ◽  
Object Trajectories ◽  
Grid Based ◽  
Moving Object Trajectories

scholarly journals A Grid-Based Swarm Intelligence Algorithm for Privacy-Preserving Data Mining

2019 ◽  
Vol 9 (4) ◽  
pp. 774 ◽  
Author(s):  
Tsu-Yang Wu ◽  
Jerry Lin ◽  
Yuyu Zhang ◽  
Chun-Hao Chen
Keyword(s):  
Data Mining ◽  
Side Effects ◽  
Evolutionary Process ◽  
Privacy Preserving ◽  
Optimal Solutions ◽  
Confidential Information ◽  
Nsga Ii ◽  
Privacy Preserving Data Mining ◽  
Single Objective ◽  
Grid Based

Privacy-preserving data mining (PPDM) has become an interesting and emerging topic in recent years because it helps hide confidential information, while allowing useful knowledge to be discovered at the same time. Data sanitization is a common way to perturb a database, and thus sensitive or confidential information can be hidden. PPDM is not a trivial task and can be concerned an Non-deterministic Polynomial-time (NP)-hard problem. Many algorithms have been studied to derive optimal solutions using the evolutionary process, although most are based on straightforward or single-objective methods used to discover the candidate transactions/items for sanitization. In this paper, we present a multi-objective algorithm using a grid-based method (called GMPSO) to find optimal solutions as candidates for sanitization. The designed GMPSO uses two strategies for updating gbest and pbest during the evolutionary process. Moreover, the pre-large concept is adapted herein to speed up the evolutionary process, and thus multiple database scans during each evolutionary process can be reduced. From the designed GMPSO, multiple Pareto solutions rather than single-objective algorithms can be derived based on Pareto dominance. In addition, the side effects of the sanitization process can be significantly reduced. Experiments have shown that the designed GMPSO achieves better side effects than the previous single-objective algorithm and the NSGA-II-based approach, and the pre-large concept can also help with speeding up the computational cost compared to the NSGA-II-based algorithm.

A Perturbation Method Based on Singular Value Decomposition and Feature Selection for Privacy Preserving Data Mining

2014 ◽  
Vol 10 (1) ◽  
pp. 55-76 ◽  
Author(s):  
Mohammad Reza Keyvanpour ◽  
Somayyeh Seifi Moradi
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Singular Value Decomposition ◽  
Perturbation Method ◽  
Privacy Preserving ◽  
Singular Value ◽  
Privacy Preserving Data Mining ◽  
Selection For ◽  
Value Decomposition ◽  
Different Levels

In this study, a new model is provided for customized privacy in privacy preserving data mining in which the data owners define different levels for privacy for different features. Additionally, in order to improve perturbation methods, a method combined of singular value decomposition (SVD) and feature selection methods is defined so as to benefit from the advantages of both domains. Also, to assess the amount of distortion created by the proposed perturbation method, new distortion criteria are defined in which the amount of created distortion in the process of feature selection is considered based on the value of privacy in each feature. Different tests and results analysis show that offered method based on this model compared to previous approaches, caused the improved privacy, accuracy of mining results and efficiency of privacy preserving data mining systems.

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