Privacy and Utility in Data Publishing with Full Functional Dependencies
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2013 by IJETT Journal|
|Year of Publication : 2013|
|Authors : P.V.N. Prasoona , M. Vasumathi Devi , K.V. Narasimha Reddy|
P.V.N. Prasoona , M. Vasumathi Devi , K.V. Narasimha Reddy. "Privacy and Utility in Data Publishing with Full Functional Dependencies". International Journal of Engineering Trends and Technology (IJETT). V4(5):1961-1964 May 2013. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group.
A number of methods have recently been proposed for privacy preserving data mining of multidimensional data records. One of the methods for privacy preserving data mining is that of anonymization, in which a record is released only if it is indistinguisha ble from k other entities in the data. Data publishing has generated much concern on individual privacy. Recent work has shown that different background knowledge can bring various threats to the privacy of published data. We distinguish the safe FFDs that will not jeopardize privacy from the unsafe ones. We design robust algorithms that can efficiently anonymize the microdata with low information loss when the unsafe FFDs are present. Our results clarify several common misconceptions about data utility and provide data publishers useful guidelines on choosing the right tradeoff between privacy and utility.
  M. Atallah, E. Bertino, A. Elmagarmid, M. Ibr ahim, V. Verykios, Disclosure limitation of sensitive rules, Workshop on Knowledge and Data Engineering Exchange (KDEX), 1999, pp. 45 – 52.
  M. Atzori, F. Bonchi, F. Giannotti, D. Pedresch, K - anonymous patterns, Proceedings of the Ninth European Conferenc e on Principles and Practice of Knowledge Discovery in Databases (PKDD), 2005, pp. 10 – 21
  R.J. Bayardo, R. Agrawal, Data privacy through optimal k - anonymization, Proceedings of the International Conference on Data Engineering (ICDE), 2005, pp. 217 – 228.
  S. Chawla, C. Dwork, F. McSherry, A. Smith, H. Wee, Toward privacy in public databases, Second Theory of Cryptography Conference (TCC), 2005, pp. 363 – 385.
  B. - C. Chen, K. LeFevre, R. Ramakrishnan, Privacy skyline: privacy with multidimensional adve rsarial knowledge, Proceedings of the International Conference on Very Large Data Bases (VLDB), 2007, pp. 770 – 781.
  T. Dalenius, S.P. Reiss, Data swapping: a technique for disclosure control, Journal of Statistical Planning and Inference, 1982.
  W. D u, Z. Teng, Z. Zhu, Privacy - maxent: integrating background knowledge in privacy quantification, Proceedings of ACM International Conference on Special Interest Group on Management of Data (SIGMOD), 2008, pp. 459 – 472.
  A. Evfimievski, J. Gehrke, R. Srika nt, Limiting privacy breaches in privacy preserving data mining, Proceedings of ACM Symposium on Principles of Database Systems (PODS), 2003, pp. 211 – 222.
  B. C. M. Fung, K. Wang, R. Chen, and P. S. Yu. Privacy - preserving data publishing: A survey on re cent developments. ACM Computing Survey , 2009.
  B. C. M. Fung, K. Wang, and P. S. Yu. Top - down specialization for information and privacy preservation. In ICDE , pages 205 – 216, 2005.
  J. Han, J. Pei, and Y. Yin. Mining frequent patterns without cand idate generation. In SIGMOD , pages 1 – 12, 2000.
  V. S. Iyengar. Transforming data to satisfy privacy constraints. In KDD , pages 279 – 288, 2002.
  D. Kifer and J. Gehrke. Injecting utility into anonymized datasets. In SIGMOD , pages 217 – 228, 2006
Privacy - preserving, data publishing, functional dependency, utility, data reconstruction.