Personally identifiable information (PII) can find its way into cyberspace through various channels, and many potential sources leak such information. Data sharing (e.g. cross-agency data sharing) for machine learning analytics is one of the important components in science. However, due to privacy concerns, should be enforced with strong guarantees before sharing. Different privacy-preserving a...

As part of the ongoing effort of designing secure DCOP algorithms, we propose P-MAX-SUM, the first private algorithm that is based on MAX-SUM. The proposed algorithm has multiple agents preforming the role of each node in the factor graph, on which the MAX-SUM algorithm operates. P-MAXSUM preserves three types of privacy: topology privacy, constraint privacy, and assignment/decision privacy. By...

Privacy preservation in data mining demands protecting both input and output privacy. The former refers to sanitizing the raw data itself before performing mining. The latter refers to preventing the mining output (model/pattern) from malicious pattern-based inference attacks. The preservation of input privacy does not necessarily lead to that of output privacy. This work studies the problem of...