document: Efficient anonymity-preserving data collection The output of a data mining algorithm is only as good as its inputs , and individuals are often unwilling to provide accurate data about sensitive topics such as medical history and personal finance . Individuals maybe willing to share their data , but only if they are assured that it will be used in an aggregate study and that it can not be linked back to them . Protocols for anonymity-preserving data collection provide this assurance , in the absence of trusted parties , by allowing a set of mutually distrustful respondents to anonymously contribute data to an untrusted data miner . To effectively provide anonymity , a data collection protocol must be collusion resistant , which means that even if all dishonest respondents collude with a dishonest data miner in an attempt to learn the associations between honest respondents and their responses , they will be unable to do so . To achieve collusion resistance , previously proposed protocols for anonymity-preserving data collection have quadratically many communication rounds in the number of respondents , and employ ( sometimes incorrectly ) complicated cryptographic techniques such as zero-knowledge proofs . We describe a new protocol for anonymity-preserving , collusion resistant data collection . Our protocol has linearly many communication rounds , and achieves collusion resistance without relying on zero-knowledge proofs . This makes it especially suitable for data mining scenarios with a large number of respondents .
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