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Anandha Mala, G. S.
- Anonymization by Data Relocation Using Sub-clustering for Privacy Preserving Data Mining
Authors
1 Sathyabama University, Chennai, Tamil Nadu, IN
2 Department of Computer Science and Engineering, St. Joseph’s College of Engineering, Chennai, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 7, No 7 (2014), Pagination: 975-980Abstract
As there are new techniques growing to reveal the hidden information on data, the threat towards those data also increases. Therefore, privacy preservation in data mining is an emerging research area which develops various algorithms to anonymize the data provided for data mining. The existing methodology handles the tradeoff between utility and privacy of data in a more expensive way in terms of execution time. In this paper, a simple Anonymization technique using subclustering is specified which achieves maximum privacy and also utility with minimum execution time. The methodology is explained with algorithm and the results are compared with the baseline method.Keywords
Anonymization, Clustering, Isometric Transformation, Privacy PreservationFull Text
- Reconstruction Based Privacy Preservation in Centralized Incremental Data Mining
Authors
1 Sathyabama University, Chennai, IN
2 St. Joseph's College of Engineering, Chennai, IN
Source
Data Mining and Knowledge Engineering, Vol 4, No 8 (2012), Pagination: 424-427Abstract
The technological development has led the storage of data efficiently in terms of both storage and cost. This huge amount of data can be used for research to reveal the hidden information for betterment of life. Since the data may hinder the privacy of individual, there is an emerging research area going on towards privacy preservation of data. When many are concentrating on static data, this is a work developed solely for incremental data where in the database gets updated frequently. The method groups the data into various classes and the encryption is based on the key values generated within each class. Since the key is not a constant private or public key, the method provides a greater amount of protection compared to usual cryptographic techniques. In this paper RPPCID, the algorithm is specified with a sample input and output database. The method does not require the execution of entire database after insertion. The method is a combination of cryptography and perturbation methodology and hence has the advantages of both. The performance of the methodology is also expressed using a graph.Keywords
Privacy Preservation, Encryption, Incremental Data, Privacy Attacks, Sequential Data Collection.
- An Estimation of Privacy in Incremental Data Mining
Authors
1 Department of Information Technology, Sathyabama University, Chennai, IN
2 Department of Computer Science and Engineering, St. Joseph’s College of Engineering, Chennai, IN
3 Tata Consultancy Services, Chennai, IN
Source
Data Mining and Knowledge Engineering, Vol 2, No 4 (2010), Pagination:Abstract
Data are values of qualitative or quantitative variables, belonging to a set of items. In recent years, advances in hardware technology have lead to an increase in the capability to store and record personal data about consumers and individuals. This has lead to concerns that the personal data may be misused for a variety of purposes. Data explains a business transaction, a medical record, bank details, educational details etc., Use of technology for data collection and analysis has seen an unprecedented growth in the last couple of decades. Such information includes private details, which the owner doesn’t want to disclose. Such data are the sources for data mining. Data mining gives us “facts” that are not obvious to human analysts of the data. When such sensitive data are given directly for mining, the security of the individual is highly affected. So the data are modified and presented for data mining. But the problem is that the altered data should also produce a similar mining result. This has lead an area called privacy preservation in datamining which is an intersection of data mining and information security. The fact in this area is the additional task which is used to implement the privacy degrades the performance of the data mining algorithm, which results in incorrect mining results. This crucial situation has led to the development of this paper which deals with the data metrics that determines the quality of the following existing privacy preserving algorithms viz., Correlation- aware Anonymization of High-dimensional Data (CAHD) [1], Privacy-Preserving Outlier Detection Through Random Nonlinear Data Distortion (PRND) [2], Privacy-Preserving Data Aggregation(PPDA) [3], Privacy-Preserving Incremental Data sets( PRID) [4] which defines various methods for implementing privacy in incremental data. Major metrics like data utility, privacy and computational time are considered for evaluation and their detailed performance is discussed.