Off-campus UMass Amherst users: To download dissertations, please use the following link to log into our proxy server with your UMass Amherst user name and password.

Non-UMass Amherst users, please click the view more button below to purchase a copy of this dissertation from Proquest.

(Some titles may also be available free of charge in our Open Access Dissertation Collection, so please check there first.)

Enabling accurate analysis of private network data

Michael G Hay, University of Massachusetts Amherst


This dissertation addresses the challenge of enabling accurate analysis of network data while ensuring the protection of network participants' privacy. This is an important problem: massive amounts of data are being collected (facebook activity, email correspondence, cell phone records), there is huge interest in analyzing the data, but the data is not being shared due to concerns about privacy. Despite much research in privacy-preserving data analysis, existing technologies fail to provide a solution because they were designed for tables, not networks, and cannot be easily adapted to handle the complexities of network data. We develop several technologies that advance us toward our goal. First, we develop a framework for assessing the risk of publishing a network that has been “anonymized.” Using this framework, we show that only a small amount of background knowledge about local network structure is needed to re-identify an “anonymous” individual. This motivates our second contribution: an algorithm that transforms the structure of the network to provably lower re-identification risk. In comparison with other algorithms, we show that our approach more accurately preserves important features of the network topology. Finally, we consider an alternative paradigm, in which the analyst can analyze private data through a carefully controlled query interface. We show that the degree sequence of a network can be accurately estimated under strong guarantees of privacy.

Subject Area

Computer science

Recommended Citation

Hay, Michael G, "Enabling accurate analysis of private network data" (2010). Doctoral Dissertations Available from Proquest. AAI3427533.