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 may be 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 quadratica...

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Justin Brickell, Vitaly Shmatikov
• Keywords: Collusion Resistant Data, Data Mining, Data Mining Permission, Dishonest Data Miner, KDD 2006