Achieving privacy in data mining using normalization

Indian Journal of Science and Technology

Volumn 6, Issue 4, 2013, Pages 4268-4272

  Manikandan, G ^a   Sairam, N ^a   Sharmili, S ^a   Venkatakrishnan, S ^a  

^a SASTRA UNIVERSITY   (India)

Author keywords

Accuracy; Clustering; K means; Min max normalization; Privacy

Indexed keywords

EID: 84877680134     PISSN: 09746846     EISSN: 09745645     Source Type: Journal    
DOI: None     Document Type: Article

References (8)

1. Rajalaxmi R R, and Natarajan A M (2008). An effective data transformation approach for privacy preserving clustering, Journal of Computer Science, vol 4(4), 320-326.
2. Karthikeyan B, Manikandan G et al. (2011). A fuzzy based approach for privacy preserving clustering, Journal of Theoretical and applied information Technology, vol 32(2), 118-122.
3. Manikandan G, Sairam N et al. (2012). Privacy preserving clustering by shearing based data transformation, Proceedings of International Conference on Computing and Control Engineering.
4. Liu L, Yang K et al. (2012). Using noise addition method based on pre-mining to protect health care privacy, Journal of Control Engineering and Applied Informatics, vol 14(2), 58-64.
5. Doganay M, Pederson T et al. (2008). Distributed privacy preserving k-means clustering with additive secret sharing, PAIS '08 Proceedings of the International Workshop on Privacy and Anonymity in Information Society, 3-11.
6. Rajalakshmi M, and Purusothaman T (2011). Privacy preserving distributed data mining using randomized site selection, European Journal Of Scientific Research, vol 64(2), 610-624.
7. nd Edn. San Francisco: Morgan Kaufmann Publishers.
8. Available from http://archive.ics.uci.edu/ml/datasets.html UCI Data Repository.