Protecting Data Privacy Through Hard-to-Reverse Negative Databases

  • Fernando Esponda
  • Elena S. Ackley
  • Paul Helman
  • Haixia Jia
  • Stephanie Forrest
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4176)


The paper extends the idea of negative representations of information for enhancing privacy. Simply put, a set DB of data elements can be represented in terms of its complement set. That is, all the elements not in DB are depicted and DB itself is not explicitly stored.

review the negative database (NDB) representation scheme for storing a negative image compactly and propose a design for depicting a multiple record DB using a collection of NDBs—in contrast to the single NDB approach of previous work. Finally, we present a method for creating negative databases that are hard to reverse in practice, i.e., from which it is hard to obtain DB, by adapting a technique for generating 3-SAT formulas.


Hard Instance Negative Representation Protect Data Privacy Query Restriction Negative Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    D. Achlioptas, Beame, and Molloy. A sharp threshold in proof complexity. In STOC: ACM Symposium on Theory of Computing (STOC), 2001.Google Scholar
  2. 2.
    Achlioptas, D., Gomes, C., Kautz, H., Selman, B.: Generating satisfiable problem instances. In: Proceedings of AAAI 2000 and IAAI 2000, pp. 256–261. AAAI Press, Menlo Park (July 30–3, 2000)Google Scholar
  3. 3.
    Achlioptas, D., Peres: The threshold for random k-SAT is 2k log 2 - O(k). JAMS: Journal of the American Mathematical Society 17 (2004)Google Scholar
  4. 4.
    Adam, N.R., Wortman, J.C.: Security-control methods for statistical databases. ACM Computing Surveys 21(4), 515–556 (1989)CrossRefGoogle Scholar
  5. 5.
    Agrawal, D., Aggarwal, C.C.: On the design and quantification of privacy preserving data mining algorithms. In: Symposium on Principles of Database Systems, pp. 247–255 (2001)Google Scholar
  6. 6.
    Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: Proc. of the ACM SIGMOD Conference on Management of Data, pp. 439–450. ACM Press, New York (2000)CrossRefGoogle Scholar
  7. 7.
    Benaloh, J.C., de Mare, M.: One-way accumulators: A decentralized alternative to digital signatures. In: Helleseth, T. (ed.) EUROCRYPT 1993. LNCS, vol. 765, pp. 274–285. Springer, Heidelberg (1994)CrossRefGoogle Scholar
  8. 8.
    Blakley, G.R., Meadows, C.: A database encryption scheme which allows the computation of statistics using encrypted data. In: Proceedings of the IEEE Symposium on Research in Security and Privacy, pp. 116–122. IEEE CS Press, Los Alamitos (1985)Google Scholar
  9. 9.
    Blum, M., Goldwasser, S.: An efficient probabilistic public-key encryption scheme which hides all partial information. In: Blakely, G.R., Chaum, D. (eds.) CRYPTO 1984. LNCS, vol. 196, pp. 289–299. Springer, Heidelberg (1985)CrossRefGoogle Scholar
  10. 10.
    Camenisch, J.L., Lysyanskaya, A.: Dynamic accumulators and application to efficient revocation of anonymous credentials. In: Yung, M. (ed.) CRYPTO 2002. LNCS, vol. 2442, pp. 61–76. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  11. 11.
    Chin, F.: Security problems on inference control for sum, max, and min queries. J. ACM 33(3), 451–464 (1986)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Cook, S.A., Mitchell, D.G.: Finding hard instances of the satisfiability problem: A survey. In: Du, Gu, Pardalos (eds.) Satisfiability Problem: Theory and Applications, Dimacs Series in Discrete Mathematics and Theoretical Computer Science, vol. 35, pp. 1–17. American Mathematical Society (1997)Google Scholar
  13. 13.
    Denning, D.: Cryptography and Data Security. AddisonWesley, Reading (1982)MATHGoogle Scholar
  14. 14.
    Denning, D.E., Schlorer, J.: Inference controls for statistical databases. Computer 16(7), 69–82 (1983)CrossRefGoogle Scholar
  15. 15.
    Dobkin, D., Jones, A., Lipton, R.: Secure databases: Protection against user influence. ACM Transactions on Database Systems 4(1), 97–106 (1979)CrossRefGoogle Scholar
  16. 16.
    Esponda, F.: Negative Representations of Information. PhD thesis, University of New Mexico (2005)Google Scholar
  17. 17.
    Esponda, F., Ackley, E.S., Forrest, S., Helman, P.: Online negative databases. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 175–188. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  18. 18.
    Esponda, F., Ackley, E.S., Forrest, S., Helman, P.: Online negative databases (with experimental results). International Journal of Unconventional Computing 1(3), 201–220 (2005)Google Scholar
  19. 19.
    Esponda, F., Forrest, S., Helman, P.: Enhancing privacy through negative representations of data. Technical report, University of New Mexico (2004)Google Scholar
  20. 20.
    Esponda, F., Forrest, S., Helman, P.: Negative representations of information. International Journal of Information Security (submitted, 2004)Google Scholar
  21. 21.
    Even, S., Yacobi, Y.: Cryptography and np-completeness. In: de Bakker, J.W., van Leeuwen, J. (eds.) ICALP 1980. LNCS, vol. 85, pp. 195–207. Springer, Heidelberg (1980)Google Scholar
  22. 22.
    Feigenbaum, J., Grosse, E., Reeds, J.A.: Cryptographic protection of membership lists  9(1), 16–20 (1992)Google Scholar
  23. 23.
    Feigenbaum, J., Liberman, M.Y., Wright, R.N.: Cryptographic protection of databases and software. In: Distributed Computing and Cryptography, pp. 161–172. American Mathematical Society (1991)Google Scholar
  24. 24.
    Fiorini, C., Martinelli, E., Massacci, F.: How to fake an RSA signature by encoding modular root finding as a SAT problem. Discrete Appl. Math. 130(2), 101–127 (2003)MATHCrossRefMathSciNetGoogle Scholar
  25. 25.
    Gent, I.P., Walsh, T.: The SAT phase transition. In: Proceedings of the Eleventh European Conference on Artificial Intelligence (ECAI 1994), pp. 105–109 (1994)Google Scholar
  26. 26.
    Goldreich, O.: Foundations of Cryptography: Basic Tools. Cambridge University Press, Cambridge (2000)Google Scholar
  27. 27.
    Goldwasser, S., Micali, S.: Probabilistic encryption. Journal of Computer and System Sciences 28(2), 270–299 (1984)MATHCrossRefMathSciNetGoogle Scholar
  28. 28.
    Impagliazzo, R., Levin, L.A., Luby, M.: Pseudo-random generation from one-way functions. In: Proceedings of the twenty-first annual ACM symposium on Theory of computing, pp. 12–24. ACM Press, New York (1989)CrossRefGoogle Scholar
  29. 29.
    Impagliazzo, R., Naor, M.: Efficient cryptographic schemes provably as secure as subset sum. In: 30th annual Symposium on Foundations of Computer Science, Research Triangle Park, NC, 1109 Spring Street, Suite 300, Silver Spring, MD 20910, USA, pp. 236–241. IEEE Computer Society Press, Los Alamitos (October 30–November 1, 1989)CrossRefGoogle Scholar
  30. 30.
    Jia, H., Moore, C., Strain, D.: Generating hard satisfiable formulas by hiding solutions deceptively. In: AAAI (2005)Google Scholar
  31. 31.
    Kautz, H.A., Ruan, Y., Achlioptas, D., Gomes, C., Selman, B., Stickel, M.E.: Balance and filtering in structured satisfiable problems. In: IJCAI, pp. 351–358 (2001)Google Scholar
  32. 32.
    Matloff, N.S.: Inference control via query restriction vs. data modification: a perspective. In: On Database Security: Status and Prospects, pp. 159–166. North-Holland Publishing Co., Amsterdam (1988)Google Scholar
  33. 33.
    Merkle, R.C., Hellman, M.E.: Hiding information and signatures in trapdoor knapsacks.  IT-24, 525–530 (1978)Google Scholar
  34. 34.
    Micali, S., Rabin, M., Kilian, J.: Zero-knowledge sets. In: Proc. FOCS 2003, p. 80 (2003)Google Scholar
  35. 35.
    Mitchell, D., Selman, B., Levesque, H.: Problem solving: Hardness and easiness - hard and easy distributions of SAT problems. In: Proceeding of (AAAI 1992), pp. 459–465. AAAI Press, Menlo Park, California (1992)Google Scholar
  36. 36.
    Naor, M., Yung, M.: Universal one-way hash functions and their cryptographic applications. In: Proceedings of the Twenty First Annual ACM Symposium on Theory of Computing, Seattle, Washington, pp. 33–43. ACM Press, New York (May 15–17, 1989)CrossRefGoogle Scholar
  37. 37.
    Odlyzko, A.M.: The rise and fall of knapsack cryptosystems. In: Pomerance, C., Goldwasser, S. (eds.) Cryptology and Computational Number Theory, Proceedings of symposia in applied mathematics. AMS short course lecture notes, vol. 42, pp. 75–88. pub-AMS (1990)Google Scholar
  38. 38.
    Ostrovsky, R., Rackoff, C., Smith, A.: Efficient consistency proofs for generalized queries on a committed database. In: Díaz, J., Karhumäki, J., Lepistö, A., Sannella, D. (eds.) ICALP 2004. LNCS, vol. 3142, pp. 1041–1053. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  39. 39.
    Shaw, P., Stergiou, K., Walsh, T.: Arc consistency and quasigroup completion. In: Proceedings of ECAI 1998 Workshop on Non-binary Constraints (1998)Google Scholar
  40. 40.
    Tendick, P., Matloff, N.: A modified random perturbation method for database security. ACM Trans. Database Syst. 19(1), 47–63 (1994)CrossRefGoogle Scholar
  41. 41.
    Wayner, P.: Translucent Databases. Flyzone Press (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fernando Esponda
    • 1
  • Elena S. Ackley
    • 2
  • Paul Helman
    • 2
  • Haixia Jia
    • 2
  • Stephanie Forrest
    • 2
  1. 1.Department of Computer ScienceYale UniversityNew Haven
  2. 2.Department of Computer ScienceUniversity of New MexicoAlbuquerque

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