Abstract
The wide adoption of the Internet has made it a convenient and low-cost platform for large-scale data collection. However, privacy has been the one issue that concerns Internet users much more than reduced costs and ease of use. When sensitive information are involved, respondents in online data collection are especially reluctant to provide truthful response, and the conventional practice to employ a trusted third party to collect the data is unacceptable in these situations. Researchers have proposed various anonymity-preserving data collection techniques in recent years, but the current methods are generally unable to resist malicious attacks adequately, and they are not sufficiently scalable for the potentially large numbers of respondents involved in online data collections. In this paper, we present an efficient anonymity-preserving data collection protocol that is suitable for mutually distrusting respondents to submit their responses to an untrusted data collector. Our protocol employs the onion route approach to unlink the responses from the respondents to preserve anonymity. Our experimental results show that the method is highly efficient and robust for online data collection scenarios that involve large numbers of respondents.
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References
Agrawal, R., Evfimievski, A., Srikant, R.: Information sharing across private databases. In: Proc. of the SIGMOD 2003 (2003)
Brickell, J., Shamatikov, V.: Efficient Anonymity-Preserving Data Collection. In: Proc. of the 12th ACM SIGKDD, pp. 76–85 (August 2006)
Yang, Z., Zhong, S., Wright, R.N.: Anonymity-preserving data collection. In: Proc. of the ACM SIGKDD, pp. 21–24 (August 2005)
Golle, P., McSherry, F., Mironov, I.: Data Collection With Self-Enforcing Privacy. In: Proc. of the ACM CCS, pp. 69–78 (2006)
Chaum, D.: Untraceable electronic mail, return addresses, and digital pseudonyms. Communications of the ACM 24, 84–90 (1981)
Chaum, D.: The dining cryptographers problem: unconditional sender and recipient untraceability. Journal of Cryptology 1, 65–75 (1988)
Warner, S.L.: Randomized response: A survey technique for eliminating evasive answer bias. The American Statistical Association 60, 63–69 (1965)
Evfimievski, A., Srikant, R., Agrawal, R., Gehrke, J.: Privacy preserving mining of association rules. In: Proc. of the ACM SIGKDD (July 2002)
Ambainis, A., Jakobsson, M., Lipmaa, H.: Cryptographic randomized response techniques. In: Bao, F., Deng, R., Zhou, J. (eds.) PKC 2004. LNCS, vol. 2947, pp. 425–438. Springer, Heidelberg (2004)
Ahn, L.V., Bortz, A., Hopper, N.J.: k-anonymous message transmission. In: Proceedings of the 10th ACM CCS (2003)
Levine, B.N., Shields, C.: Hordes: a multicast based protocol for anonymity. Journal of Computer Security 10, 213–240 (2002)
Reiter, M.K., Rubin, A.D.: Crowds: Anonymity for Web transactions. ACM Transactions on Information and System Security 1, 66–92 (1998)
Syverson, P.F., Goldschlag, D.M., Reed, M.G.: Anonymous Connections and Onion Routing. In: Proc. of the IEEE Symp. on S&P, p. 44 (1997)
Evfimievski, J.G., Srikant, R.: Limiting privacy breaches in privacy preserving data mining. In: Proc. of the 22nd ACM SIGMOD, pp. 211–222 (June 2003)
Dingledine, R., Mathewson, N., Syverson Tor, P.: Second Generation Data Mining Onion Route. In: Proc. of the 13th USENIX Security Symp. (2004)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. John Wiley & Sons, Inc., Chichester (1991)
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Ashrafi, M.Z., Ng, S.K. (2009). Efficient and Anonymous Online Data Collection. In: Zhou, X., Yokota, H., Deng, K., Liu, Q. (eds) Database Systems for Advanced Applications. DASFAA 2009. Lecture Notes in Computer Science, vol 5463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00887-0_41
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DOI: https://doi.org/10.1007/978-3-642-00887-0_41
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