document: Anonymity-preserving data collection Protection of privacy has become an important problem in data mining . In particular , individuals have become increasingly unwilling to share their data , frequently resulting in individuals either refusing to share their data or providing incorrect data . In turn , such problems in data collection can affect the success of data mining , which relies on sufficient amounts of accurate data in order to produce meaningful results . Random perturbation and randomized response techniques can provide some level of privacy in data collection , but they have an associated cost in accuracy . Cryptographic privacy-preserving data mining methods provide good privacy and accuracy properties . However , in order to be efficient , those solutions must be tailored to specific mining tasks , thereby losing generality . In this paper , we propose efficient cryptographic techniques for online data collection in which data from a large number of respondents is collected anonymously , without the help of a trusted third party . That is , our solution allows the miner to collect the original data from each respondent , but in such a way that the miner can not link a respondent 's data to the respondent . An advantage of such a solution is that , because it does not change the actual data , its success does not depend on the underlying data mining problem . We provide proofs of the correctness and privacy of our solution , as well as experimental data that demonstrates its efficiency . We also extend our solution to tolerate certain kinds of malicious behavior of the participants .
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