Privacy-Preserving Data Mining

1. A. Evfimievski, J. E. Gehrke, and R. Srikant. Limiting Privacy Breaches in Privacy Preserving Data Mining. In Proceedings of the 22nd ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS 2003). San Diego, CA. June 2003. The authors define privacy breach as a property by which some private information of a client can be found by the server with high probability. They provide a new technique "Amplification" which ensures limitation on privacy breaches which occur using normal randomization technique while protecting privacy. The authors claim to produce high quality limitation of privacy breach without the information regarding the distribution of the data.

2. A. Evfimievski, R. Srikant, R. Agrawal and J. Gehrke. Privacy Preserving Mining of Association Rules. Proc. of 8th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining (KDD). July 2002. "Uniform" randomization of the data could preserve privacy in data, but they could be exploited to find information in the data. So here they have shown few randomization techniques (e.g. cut and paste randomization) which could help preserving data better than the uniform randomization techniques.

3. B. Pinkas. Cryptographic techniques for privacy preserving data mining. SIGKDD Explorations, 4(2). Dec. 2002. They consider the existence of data in a distributed environment rather than a central repository. They show that it is difficult to design an implementation for multi-party construction than two-party construction. They have used the ID3 algorithm for construction of the decision trees for classification of the data in the dataset.

4. C. Clifton, M. Kantarcioglu, J. Vaidya, X. Lin and M. Y. Zhu. Tools for Privacy Preserving Distributed Data Mining. In SIGKDD Explorations, 4(2). 28-34 December 2002. In this paper the authors believe that a toolkit of privacy preserving computations can be helpful in designing Data mining techniques. They provide the components of the toolkit, techniques like Secure sum (computes sum securely), Secure set union (union is created and shared securely), Secure size of intersection (intersection of datasets are created and shared securely) and Scalar product (calculating scalar product of two vectors securely). They also present various applications for these privacy preserving data mining solutions.

5. C. Farkas and S. Jajodia. The Inference Problem: A Survey. In SIGKDD Explorations, 4(2). 6-11, December 2002. The authors provide a survey of current and emerging research in data inference control. They provide reviews on the inference problem, in general purpose databases, data mining and web based applications. They also relate the inference control problem to secure communication and mobile-computing.

6. D. Agrawal and C. C. Aggarwal. On the design and quantification of privacy preserving data mining algorithms. In Proceedings of the Twentieth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. Santa Barbara, California, USA. May 21-23 2001. ACM. The authors show that there is a natural loss of information during distribution reconstruction process from the perturbed data which is created to preserve privacy. Here they provide Expectation Maximization (EM) algorithm, which they show is better than the other available techniques. They show that EM algorithm converges to Maximum-Likelihood estimate of the original distribution, thereby preserving privacy better.

7. D. E. O'Leary. Some Privacy Issues in Knowledge Discovery: The OECD Personal Privacy Guidelines. In IEEE Expert, v.10, n.2. April 1995. pp.48-52. In this paper the author discuss about the legal systems influencing the knowledge discovery from the databases. The author provides a brief note on various OECD (Organization for Economic Cooperation and Development) principles and relates them to the Knowledge discovery (Data mining). The author also provides the differences between the knowledge discovery of the individual against group information.

8. H. Kargupta, S. Datta, Q. Wang,and K. Sivakumar. On the Privacy Preserving Properties of Random Data Perturbation Techniques. In Proceedings of the Third IEEE International Conference on Data Mining (ICDM'03). Melbourne, Florida, USA. November 2003. pp. 99-106. The authors in this paper questions the usage of the randomization technique for privacy preserving in data mining techniques, they show that randomization does not help completely in preserving privacy. They show that original data could be retireved from the randomized dataset. They also provide explicitly the assumptions made in preserving privacy in exisiting systems and provide ways by which a general framework could be provided for preserving privacy better.

9. I. Dinur, K. Nissim. Revealing information while preserving privacy. Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. California. 2003. pp.202–210. For protecting privacy the authors specify that the designer of the privacy preserving system has to provide a balance between hiding the privacy functions or revealing the information functions to the end user. They show various situations and kinds of queries provided to the database for retrieval of information (E.g. adversary issuing all possible queries to find information from a small database). They also discuss privacy preserving in the case of a time / query bounded adversary and provide algorithms where the perturbation magnitude is directly dependent on time or query.

10. J. Vaidya and C. Clifton. Privacy Preserving Association Rule Mining in Vertically Partitioned Data. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton, AB, Canada. July 2002. pp 639-644. The authors provide a privacy preserving association rule mining algorithm, which they show works efficiently while the data is distributed across many locations. They show that with reasonable communication cost, one could achieve good privacy protection in distributed data setting.

11. M. Kantarcioglu and C. Clifton. Privacy-preserving distributed mining of association rules on horizontally partitioned data. In The ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD'02). June 2 2002. In this paper authors discuss about the cryptographic techniques to minimize the information shared, while adding little overhead to the mining task. They address the issue with the help of a scenario; some parties were allowed to access some of the data. Whereas, other existing techniques were using a scenario in which the values were kept private from anybody who is performing the mining to show the efficiency of their algorithm.

12. M. Kantarcioglu, J. Jin, and C. Clifton. When Do Data Mining Results Violate Privacy? In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2004). Seattle. WA, USA. August 2004. Privacy Preserving Data Mining techniques are used to protect results protecting privacy of the data in the datasets. This paper explores the area of "Do the results themselves violate privacy?" This paper presents methods and matrices in evaluating various Privacy Data Mining techniques.

13. M. Kantarcioglu and J. Vaidya. An Architecture for Privacy-preserving Mining of Client Information. In Proceedings of the IEEE ICDM Workshop on Privacy, Security and Data Mining. Maebashi City, Japan. December 2002. pp.37-42. In this, authors have considered the databases as sites and provided three different sites which form the architecture for privacy preserving. They are: 1.Original Site (OS) where the information is collected and also will learn about the results, 2. Non-Colluding Storage Site (NSS) storing the shared part of user information and 3.Processing Site (PS) for performing the Data mining. They show that OS could not differentiate between users, NSS could not learn any information and PS will learn only the aggregate information in their architecture in the process of preserving privacy in the data.

14. N. Zang, S. Wang, and W. Zhao. A New Scheme on Privacy Preserving Association Rule Mining. In Proceedings of the 15th European Conference on Machine Learning (ECML) and the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD). Pisa, Italy. September 2004. In the paper the authors show that randomization is not the only way that privacy can be preserved in the data, they introduce algebraic methods for preserving privacy in the data. They show that their technique discloses private transaction information about five times less than the previous approaches. They use error of support of frequent item sets (false drops and false positives) as accuracy metrics and privacy breach as privacy metrics in measuring the accuracy of their system.

15. R. Agrawal and R. Srikant. Privacy-Preserving Data Mining. In Proceedings of the ACM SIGMOD Conference on Management of Data. Dallas, Texas. May 2000. pp.439-450. The authors have asked the question "can we develop accurate models without access to precise information in individual data records?", which has created a new area of research; preserving privacy in data mining procedures. They reconstruct the distribution and not the individual records from the database which has been perturbed with a randomized function for protecting privacy. By developing two techniques By Class and Local, they show that it would not be possible to obtain the true value with a small drop in accuracy, which they say it to be a desirable trade-off for privacy in many situations.

16. S. Agrawal, V. Krishnan and J. R. Haritsa. On Addressing Efficiency Concerns in Privacy-Preserving Mining. In Proceedings of the 9th International Conference on Database Systems for Advanced Applications (DASFAA-2004). Jeju Island, Korea. March 2004. People are scared to provide personal information while using websites as they feel the organization would misuse the information. To increase the confidence of the user, a system called MASK (Mining Associations with Secrecy Konstraints) was developed, where the information can be distorted at the user end using a simple probabilistic distribution instead of any third-party or the organization doing the same. They show that the efficiency of the Privacy Preserving Data Mining can be well with an order or magnitutde with respect to data mining by maintaining a satisfactory level of privacy and accuracy.

17. S. R. M. Oliveira and O. R. Zaïane. Toward Standardization in Privacy-Preserving Data Mining. In Proceedings of the 3rd. Workshop on Data Mining Standards (DM-SSP 2004), in conjunction with KDD 2004. Seattle, WA, USA. August, 2004. This paper discusses some of the required future work, where they provide steps in standardizing the Privacy Preserving Data Mining techniques. They analyze the implications of Organization for Economic Cooperation and Development (OECD) data privacy principles and propose some requirements for the development and deployment of solutions. The authors in this paper provide an overview on adopting the OECD principles for providing privacy preserving techniques in datasets.

18. V. S. Verykios, E. Bertino, I. N. Fovino, L. P. Provenza, Y. Saygin, Y. Theodoridis. State-of-the-art in Privacy Preserving Data Mining. In SIGMOD Record, 33(1): 50-57. March 2004. Authors in this paper provide a survey of the existing privacy preserving data mining techniques. They classify the techniques based on the following dimensions: data distribution, data modification, data mining algorithm, data or rule hiding and privacy preservation. They define a parameter "transversal endurance" which is used to evaluate the sanitization algorithms designed for various privacy preserving techniques in different databases.

19. Verykios, V.S. Elmagarmid, A.K. Bertino, E.; Saygin, Y. Dasseni, E. Association rule hiding Knowledge and Data Engineering. IEEE Transactions on Knowledge and Data Engineering, Volume: 16 , Issue: 4. April 2004. pp. 434 – 447. In this paper the authors provide two approaches: 1.Hiding the frequent sets to prevent the rules from being generated and 2.Reducing the importance of the rules by keeping the confidence below a threshold value. They provide five algorithms that are built on these two approaches. These strategies or algorithms perform minimal perturbation on the data values in the data set.

20. Y. Lindell and B. Pinkas. Privacy Preserving Data Mining. In Proceedings of CRYPTO 2000, LNCS 1880, Springer-Verlag. Santa Barbara, CA. August 2000. pp.36-54. In this work the authors discuss about data mining algorithm to be used for analysis from union of two confidential databases but none of each of the database entity wants to share any information with the other database. Using ID3 algorithm they show that among the parties interacting for data analysis, no party can learn anything with respect to the data other than output.

21. Y. Saygin, V. S. Verykios, and A. K. Elmagarmid. Privacy Preserving Association Rule Mining. In Proceedings of the 12th International Workshop on Research Issues in Data Engineering: Engineering E-Commerce/E-Business Systems (RIDE'02). San Jose, CA, USA. February 2002. In this paper, the authors consider one category of data mining technique, association rule mining in which they provide two algorithms for rule hiding in the datasets. The two algorithms are: 1.Focusing on hiding the rules by reducing the minimum support of the item sets that generated the rules and 2.Focusing on reducing the minimum confidence of the rules. They also show that deterministic algorithms as discussed in the paper could protect the privacy better.

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