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«Microdata Confidentiality References william.e.winkler 2008Nov12 Abowd, J. M., and Woodcock, S. D. (2002), “Disclosure Limitation in ...»

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Microdata Confidentiality References william.e.winkler@census.gov 2008Nov12

Abowd, J. M., and Woodcock, S. D. (2002), “Disclosure Limitation in Longitudinal Linked Data,” in (P. Doyle et al,

eds.) Confidentiality, Disclosure, and Data Access, Amsterdam, The Netherlands: North Holland.

Abowd, J. M., and Woodcock, S. D. (2004), “Multiply-Imputing Confidential Characteristics and File Links in

Longitudinal Linked Data, in (J. Domingo-Ferrer and V. Torra, eds.), Privacy in Statistical Databases 2004, New York: Springer.

Abowd, J. M. (2008), “How Protective are Synthetic Data,”Privacy in Statistical Databases 2008, New York:

Springer.

Adams, N. R., and Wortmann, J. C., (1989), “Security-control Methods for Statistical Databases, A Comparative Study,” ACM Computing Surveys, 21, 515-556.

Aggarwal, C. C., (2005), “On k-Anonymity and the Curse of Dimensionality,” Proceedings of the 31st VLDB Conference, Trondheim, Norway, http://www.vldb2005.org/program/paper/fri/p901-aggarwal.pdf.

Aggarwal, C. C., and Parthasarathy, S. (2001), “Mining Massively Incomplete Data Sets through Conceptual Reconstruction,” Proceedings of the ACM KDD Conference, 227-232.

Aggarwal, C. C., and Yu, P. (2004), “A Condensation Approach to Privacy Preserving Data Mining,” Proceedings of the EBDT Conference, 183-199.

Aggarwal, G., Feder, T., Kenthapadi, K., Motwani, R., Panigrahy, R., Thomas, D., and Zhu, A. (2005), “Anonymizing Tables,” International Conference on Database Theory.

Aggarwal, G., Feder, T., Kenthapadi, K., Khuller, S. Panigrahy, R., Thomas, D., and Zhu, A. (2006), “Achieving Anonymity via Clustering,” ACM PODS ’06.

Agrawal, D., and Aggarwal, C. C. (2001), “On the Design and Quantification of Privacy Preserving Data Mining Algorithms,” Association of Computing Machinery, Proceedings of PODS 2001, 247-255.

Agrawal, R., and Srikant, R. (2000), Privacy Preserving Data Mining, Proceedings of the ACM SIGMOD 2000, 439Agrawal, R., Srikant, R., and Thomas, D. (2005), “Privacy Preserving OLAP,” ACM SIGMOD Conference.

Agrawal, R., Kiernan, J., Srikant, R., and Xu, Y. (2002), “Hippocratic Databases,” Very Large Databases 2002.

Bacher, J., Bender, S., and Brand, R. (2001), “Re-identifying Register Data by Survey Data: An Empirical Study,” presented at the UNECE Workshop On Statistical Data Editing, Skopje, Macedonia, May 2001.

Bacher, J., Brand, R., and Bender, S. (2002) Re-identifying Register Data by Survey Data using Cluster Analysis: An Empirical Study, International Journal of Uncertainty, Fuzziness, Knowledge-Based Systems, 10 (5) 589-608.

Barak, B., Chaudhuri, K., Dwork, C., Kale, S., McSherry, F., and Talwar, K. (2007), “Privacy, Accuracy, and Consistency Too: A Holistic Solution to Contingency Table Release,” PODS ’07, Beijing, China.

Bayardo, R. J., and Agrawal, R. (2005), “Data Privacy Through Optimal K-Anonymization,” IEEE 2005 International Conference on Data Engineering.

Bethlehem, J. A., Keller, W. J., and Pannekoek, J., (1990), "Disclosure Control of Microdata," Journal of the American Statistical Association, 85, 38-45.

Blien, U., Wirth, U., and Muller, M. (1992), “Disclosure Risk for Microdata Stemming from Official Statistics,” Statistica Neerlandica, 46, 69-82.

Blum, A., Dwork, C., McSherry, F., and Nissim, K. (2005), “Practical Privacy: The SuLQ Framework,” ACM SIGMOD Conference (also http://research.microsoft.com/research/sv/DatabasePrivacy/bdmn.pdf ).

Brand, R. (2002), “Microdata Protection Through Noise Addition,” in (J. Domingo-Ferrer, ed.) Inference Control in Statistical Databases, New York: Springer, 97-116.

Castro, J. (2004), “Computational Experience with Minimum-Distance Controlled Perturbation Methods,” in (J.

Domingo-Ferrer, ed.), Privacy in Statistical Databses 2004, Springer: New York.

Chawla, S., Dwork, C., McSherry, F., Smith, A., and Wee, H. (2004), “Toward Privacy in Public Databases,” Microsoft Research Technical Report, Theory of Cryptography Conference.

Chawla, S., Dwork, C., McSherry, F., and Talwar, K. (2005), “On the Utility of Privacy-Preserving Histograms,” http://research.microsoft.com/research/sv/DatabasePrivacy/cdmt.pdf.

Dalenius, T., and Reiss, S.P. (1982), “Data-swapping: A Technique for Disclosure Control,” Journal of Statistical Planning and Inference, 6, 73-85.

Dandekar, R. A. (2004), Maximum Utility Minimum Information Loss Table Server Design of Statistical Disclosure

Control of Tabular Data, in (J. Domingo-Ferrer and V. Torra, eds.), Privacy in Statistical Databases, New York:

Springer, 121-135.

Dandekar, R. A., Domingo-Ferrer, J., and Sebe, F. (2002), “LHS-Based Hybrid Microdata vs Rank Swapping and Microaggregation for Numeric Microdata Protection,” in (J. Domingo-Ferrer, ed.) Inference Control in Statistical Databases, New York: Springer, 175-186.

Dandekar, R., Cohen, M., and Kirkendal, N. (2002), “Sensitive Microdata Protection Using Latin Hypercube Sampling Technique,” in (J. Domingo-Ferrer, ed.) Inference Control in Statistical Databases, New York: Springer, 117-125.

Defays, D., and Anwar, M. N. (1998), “Masking Microdata Using Micro-aggregation,” Journal of Official Statistics, 14, 449-461.





Defays, D., and Nanopolis, P. (1993), “Panels of Enterprises and Confidentiality: the Small Aggregates Method,” in Proceedings of the 1992 Symposium on Design and Analysis of Longitudinal Surveys, 195-204.

De Waal, A. G., and Willenborg, L.C.R.J. (1995), "Global Recodings and Local Suppressions in Microdata Sets," Proceedings of Statistics Canada Symposium 95, 121-132.

De Waal, A. G., and Willenborg, L.C.R.J. (1996), "A View of Statistical Disclosure Control for Microdata," Survey Methodology, 22, 95-103.

De Wolf, P.-P. (2007), “Risk, Utility and PRAM A Comparison of Proximity Swap and Data Shuffle for Numeric Data,” in (J. Domingo-Ferrer, ed.) Statistical Data Protection 2006, Springer: New York, N.Y.

Dinur, I., and Nissim, K. (2003), “Revealing Information while Preserving Privacy,” ACM PODS Conference, 202Domingo-Ferrer, J. (2001), “On the Complexity of Microaggregation,” presented at the UNECE Workshop On Statistical Data Editing, Skopje, Macedonia, May 2001.

Domingo-Ferrer, J. (ed.) (2002) Inference Control in Statistical Databases, New York: Springer Domingo-Ferrer, J., and Mateo-Sanz, J. M. (2001), “An Empirical Comparison of SDC Methods for Continuous Microdata in Terms of Information Loss And Re-Identification Risk,” presented at the UNECE Workshop On Statistical Data Editing, Skopje, Macedonia, May 2001.

Domingo-Ferrer, J., and Mateo-Sanz, J. M. (2002), “Practical Data-Oriented Microaggregation for Statistical Disclosure Control,” IEEE Transactions on Knowledge and Data Engineering, 14 (1), 189-201.

Domingo-Ferrer, J., Mateo-Sanz, J., Oganian, A., and Torres, A. (2002), “On the Security of Microaggregation with Individual Ranking: Analytic Attacks,” International Journal of Uncertainty, Fuzziness, and Knowledge-Based Systems, 477-492.

Domingo-Ferrer, J., Sebé, F., and Castellà-Roca, J. (2004), “On the Security of Noise Addition for Privacy In

Statistical Databases, in (J. Domingo-Ferrer and V. Torra, eds.), Privacy in Statistical Databases, New York:

Springer, 149-161.

Domingo-Ferrer, J., and Torra, V. (2001) A Quantitative Comparison of Disclosure Control Methods for Microdata in (P. Doyle, J. Lane, J. Theeuwes, and L. Zayatz, eds.) Confidentiality, Disclosure Control and Data Access: Theory and Practical Applications, Amsterdam, The Netherlands: North Holland, 111-134.

Domingo-Ferrer, J., and Torra, V. (2003), “Statistical Data Protection in Statistical Microdata Protection Via Advanced Record Linkage,” Statistics and Computing, 13 (4), 343-354.

Dwork, C. (2006), “Differential Privacy,” 33rd International Colloquium on Automata, Languages and Programming – ICALP 2006, Part II, 1-12.

Dwork, C. (2008), “Differential Privacy: A Survey of Results,” in (M. Agrawal et al., eds.) TAMC 2008, LNCS 4978, 1-19.

Dwork, C., McSherry, F., Nissim, K., and Smith, A. (2006), “Calibrating Noise to Sensitivity in Private Data Analysis,” 3rd Conference on Cryptography – TCC 2006, 365-384.

Dwork, C., McSherry, F., and Talwar, K. (2007a), “The Price of Privacy and the Limits of LP Decoding,” STOC ’07, San Diego, CA.

Dwork, C., McSherry, F., and Talwar, K. (2007b), “Differentially Private Marginals Release with Mutual Consistency and Error Independent Sample Size,” UNECE Worksession on Statistical Data Confidentiality, Manchester, UK,at http://www.unece.org/stats/documents/2007/12/confidentiality/wp.19.e.pdf.

Dwork, C. and Yekhanin, S. (2008), “New Efficient Attacks on Statistical Disclosure Control Mechanisms,” Advances in Cryptology—CRYPTO 2008, to appear, also at http://research.microsoft.com/research/sv/DatabasePrivacy/dy08.pdf.

Du., W., Han, Y. S., and Chen, S. (2004), “Privacy Preserving Multivariate Statistical Analysis: Linear Regression and Classification,” SIAM International Conference on Data Mining 2004.

Du, W., and Zhan, Z. (2003), “Using Randomized Response Techniques for Privacy Preserving Data Mining,” ACM Knowledge Discovery and Data Mining Conference 2003, 505-510.

DuMouchel, W., Volinsky, C., Johnson, T., Cortes, C., and Pregibon, D. (1999), “Squashing Flat Files Flatter,” Proceedings of the ACM Knowledge Discovery and Data Mining Conference, 6-15.

Duncan, G. T., Keller-McNulty, S. A., and Stokes, S. L. (2001), “Disclosure Risk vs. Data Utility: The R-U Confidentiality Map,” Los Alamos National Laboratory Technical Report LA-UR-01-6428.

Dwork, C. and Nissim, K. (2004), “Privacy-Preserving Datamining on Vertically Partitioned Databases,” Microsoft Research Technical Report.

Elamir, E. A. H. (2004), “Analysis of Re-identification Risk based on Log-Linear Model,” in (J. Domingo-Ferrer and V. Torra, eds.), Privacy in Statistical Databases, Springer: New York, 273-281.

Elamir, E. A. H. and Skinner, C. J. (2006), “Record Level Measures of Disclosure Risk for Survey Microdata,“ Journal of Official Statistics, 22, 525-539.

Elliott, M. A., Manning, A. M., and Ford, R. W. (2002), “A Computational Algorithm for Handling the Special Uniques Problem, International Journal of Uncertainty, Fuzziness, and Knowledge-Based Systems, 10 (5), 493-510.

Elliot, M.J., Skinner, C. A., and Dale, A. (1998), “Special Uniques, Random Uniques, and Sticky Populations: Some Counterintuitive Effects of Geographic Detail in Disclosure Risk,” Research in Official Statistics, 1, 53-68.

Evfimievski, A. (2004), “Privacy Preserving Information Sharing,” Ph.D. Dissertation, Cornell University, http://www.cs.cornell.edu/aevf/.

Evfimievski, A., Gehrke, J., and Srikant, R. (2003), “Limiting Privacy Breaches in Privacy Preserving Data Mining,” ACM PODS Conference, 211-222.

Evfimievski, A., Srikant, R., Agrawal, R., and Gehrke, J. (2002), “Privacy Preserving Mining of Association Rules,” Association of Computing Machinery, Special Interest Group on Knowledge Discovery and Datamining 2002.

Fellegi, I. P., and Sunter, A. B. (1969), "A Theory for Record Linkage," Journal of the American Statistical Association, 64, 1183-1210.

Fellegi, I. P. (1972), "On the Question of Statistical Confidentiality," Journal of the American Statistical Association, 67, 7-18.

Fellegi, I. P. (1999), “Record Linkage and Public Policy - A Dynamic Evolution,” Proceedings of the Record Linkage Workshop 1997, Washington, DC: National Academy Press, 3-12.

Fienberg, S. E. (1997), “Confidentiality and Disclosure Limitation Methodology: Challenges for National Statistics and Statistical Research, commissioned by Committee on National Statistics of the National Academy of Sciences.

Fienberg, S. E. and MacIntyre, J. (2005), “Data Swapping: Variations on a Theme of Dalenius and Reiss,” Journal of Official Statistics, 21 (2), 309-323.

Fienberg, S. E., Makov, E. U., and Sanil, A. P., (1997), “A Bayesian Approach to Data Disclosure: Optimal Intruder Behavior for Continuous Data,” Journal of Official Statistics, 14, 75-89.

Fienberg, S. E., Makov, E. U., and Steel, R. J. (1998), “Disclosure Limitation using Perturbation and Related Methods for Categorical Data,” Journal of Official Statistics, 14, 485-502.

Frakes, W., and Baeza-Yates, R. (1992), Information Retrieval - Data Structures and Algorithms, Upper Saddle River, NJ:.Prentice-Hall.

Franconi, L., Capobianchi, A., Polletini, S., and Seri, G. (2001), “Experiences in Model-Based Disclosure Protection,” presented at the UNECE Workshop on Statistical Data Confidentiality, Skopje, Macedonia, May 2001.

Fuller, W. A. (1993), “Masking Procedures for Microdata Disclosure Limitation,” Journal of Official Statistics, 9, 383-406 (http://www.jos.nu/Articles/abstract.asp?article=92383 ).

Fung, B. C. M., Wang, K., and Yu, P. S. (2005), Top-Down Specialization for Information and Privacy Preservation,” IEEE International Conference on Data Engineering, 205-216.

Ganta, S., Prasad, S., and Smith, A. (2008), Compositional Attacks and Auxiliary Information in Data Privacy,” ACM KDD ’08, 265-273.

Gopal, R., Goes, P. and Garfinkel, R. (1998) “Confidentiality Via Camouflage: The CVC Approach to Database Query Management,” in Statistical Data Protection ’98, Eurostat, Brussels, Belgium, 1-8. (also (2002) Operations Research, 50 (3) ).

Gilburd, B., Schuster, A., and Wolff, R. (2004b), “k-TTP: A New Privacy Model for Large-Scale Distributed Environments,” ACM Knowledge Discovery and Data Mining Conference 2004, 599-604.

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Gomatam, S. V., and Karr, A. (2003), “On Data Swapping of Categorical Data,” American Statistical Association, Proceedings of the Section on Survey Research Methods, CD-ROM.

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