scholarly journals A Review on Privacy Preservation in Data Mining

2016 ◽  
Vol 7 (3) ◽  
pp. 01-07
Author(s):  
Nandhini T ◽  
Vanathi D ◽  
Sengottuvelan P
Keyword(s):  
Data Mining ◽  
Privacy Preservation

scholarly journals A Fuzzy Logic based Privacy Preservation Clustering method for achieving K- Anonymity using EMD in dLink Model

2016 ◽  
Vol 12 (12) ◽  
pp. 4601-4610 ◽  
Author(s):  
D. Palanikkumar ◽  
S. Priya ◽  
S. Priya
Keyword(s):  
Data Mining ◽  
Privacy Preservation ◽  
Numerical Data ◽  
Fuzzy Membership Function ◽  
Data Mining Technique ◽  
Sensitive Data ◽  
Sensitive Attribute ◽  
Modification Technique ◽  
Earth Mover ◽  
Preservation Technique

Privacy preservation is the data mining technique which is to be applied on the databases without violating the privacy of individuals. The sensitive attribute can be selected from the numerical data and it can be modified by any data modification technique. After modification, the modified data can be released to any agency. If they can apply data mining techniques such as clustering, classification etc for data analysis, the modified data does not affect the result. In privacy preservation technique, the sensitive data is converted into modified data using S-shaped fuzzy membership function. K-means clustering is applied for both original and modified data to get the clusters. t-closeness requires that the distribution of sensitive attribute in any equivalence class is close to the distribution of the attribute in the overall table. Earth Mover Distance (EMD) is used to measure the distance between the two distributions should be no more than a threshold t. Hence privacy is preserved and accuracy of the data is maintained.

scholarly journals Opposition Intensity-Based Cuckoo Search Algorithm for Data Privacy Preservation

2019 ◽  
Vol 29 (1) ◽  
pp. 1441-1452 ◽  
Author(s):  
G.K. Shailaja ◽  
C.V. Guru Rao
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Original Data ◽  
Cuckoo Search Algorithm ◽  
Research Issues ◽  
Novel Approach ◽  
Two Phases

Abstract Privacy-preserving data mining (PPDM) is a novel approach that has emerged in the market to take care of privacy issues. The intention of PPDM is to build up data-mining techniques without raising the risk of mishandling of the data exploited to generate those schemes. The conventional works include numerous techniques, most of which employ some form of transformation on the original data to guarantee privacy preservation. However, these schemes are quite multifaceted and memory intensive, thus leading to restricted exploitation of these methods. Hence, this paper intends to develop a novel PPDM technique, which involves two phases, namely, data sanitization and data restoration. Initially, the association rules are extracted from the database before proceeding with the two phases. In both the sanitization and restoration processes, key extraction plays a major role, which is selected optimally using Opposition Intensity-based Cuckoo Search Algorithm, which is the modified format of Cuckoo Search Algorithm. Here, four research issues, such as hiding failure rate, information preservation rate, and false rule generation, and degree of modification are minimized using the adopted sanitization and restoration processes.

scholarly journals A survey on game theoretic approaches for privacy preservation in data mining and network security

2019 ◽  
Vol 155 ◽  
pp. 686-691 ◽  
Author(s):  
Hitarth Shah ◽  
Vishruti Kakkad ◽  
Reema Patel ◽  
Nishant Doshi
Keyword(s):  
Data Mining ◽  
Network Security ◽  
Privacy Preservation ◽  
Game Theoretic

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.

A Framework for Categorizing and Applying Privacy-Preservation Techniques in Big Data Mining

Computer ◽  
2016 ◽  
Vol 49 (2) ◽  
pp. 54-62 ◽  
Author(s):  
Lei Xu ◽  
Chunxiao Jiang ◽  
Yan Chen ◽  
Jian Wang ◽  
Yong Ren
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Privacy Preservation ◽  
Big Data Mining

Rule Extraction from Privacy Preserving Neural Network: Application to Banking

2011 ◽  
Vol 403-408 ◽  
pp. 920-928 ◽  
Author(s):  
Nekuri Naveen ◽  
V. Ravi ◽  
C. Raghavendra Rao
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Privacy Preservation ◽  
Cross Validation ◽  
Hybrid Approach ◽  
Rule Extraction ◽  
Privacy Preserving ◽  
Preservation Method ◽  
Network Application ◽  
Fold Cross Validation

In the last two decades in areas like banking, finance and medical research privacy policies restrict the data owners to share the data for data mining purpose. This issue throws up a new area of research namely privacy preserving data mining. In this paper, we proposed a privacy preservation method by employing Particle Swarm Optimization (PSO) trained Auto Associative Neural Network (PSOAANN). The modified (privacy preserved) input values are fed to a decision tree (DT) and a rule induction algorithm viz., Ripper for rule extraction purpose. The performance of the hybrid is tested on four benchmark and bankruptcy datasets using 10-fold cross validation. The results are compared with those obtained using the original datasets where privacy is not preserved. The proposed hybrid approach achieved good results in all datasets.

scholarly journals Techniques for Privacy Preservation in Data Mining

2015 ◽  
Vol V4 (10) ◽  
Author(s):  
Suchitra Shelke ◽  
Prof. Babita Bhagat ◽  
Keyword(s):  
Data Mining ◽  
Privacy Preservation
Sign in / Sign up

Export Citation Format

Share Document