Distributed Privacy Preserving Data Collection

Mingqiang Xue, Panagiotis Papadimitriou, Chedy Raïssi, Panos Kalnis, Hung Keng Pung

Abstract: We study the distributed privacy preserving data collection problem: an untrusted data collector (e.g., a medical research institute) wishes to collect data (e.g., medical records) from a group of respondents (e.g., patients). Each respondent owns a multi-attributed record which contains both non-sensitive (e.g., quasi-identifiers) and sensitive information (e.g., a particular disease), and submits it to the data collector. Assuming T is the table formed by all the respondent data records, we say that the data collection process is privacy preserving if it allows the data collector to obtain a k-anonymized or l-diversified version of T without revealing the original records to the adversary.