Re-identification is a major privacy threat to public datasets containing individual records. Many privacy protection algorithms rely on generalization and suppression of "quasi-identifier" attributes such as ZIP code and birthdate. Their objective is usually syntactic sanitization: for example, k-anonymity requires that each "quasi-identifier" tuple appear in at least k records, while l-diversity requires that the distribution of sensitive attributes for each quasi-identifier have high entropy. The utility of sanitized data is also measured syntactically, by the number of generalization steps applied or the number of records with the same quasi-identifier. In this paper, we ask whether generalization and suppression of quasi-identifiers offer any benefits over trivial sanitization which simply separates quasi-identifiers from sensitive attributes. Previous work showed that k-anonymous databases can be useful for data mining, but k-anonymization does not guarantee any privacy. By contrast, we measure the tradeoff between privacy (how much can the adversary learn from the sanitized records?) and utility, measured as accuracy of data-mining algorithms executed on the same sanitized records.

We present an efficient protocol for privacy-preserving evaluation of diagnostic programs, represented as binary decision trees or branching programs. The protocol applies a branching diagnostic program with classification labels in the leaves to the user's attribute vector. The user learns only the label assigned by the program to his vector; the diagnostic program itself remains secret. The program's owner does not learn anything. Our construction is significantly more efficient than those obtained by direct application of generic secure multi-party computation techniques.

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 cannot 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.