Title:
De-anonymizing social networks and mobility traces
De-anonymizing social networks and mobility traces
| dc.contributor.author | Li, Weiqing | |
| dc.contributor.committeeMember | Beyah, Abdul R. | |
| dc.contributor.committeeMember | Copeland, John A. | |
| dc.contributor.committeeMember | Chang, Yusun | |
| dc.contributor.department | Electrical and Computer Engineering | |
| dc.date.accessioned | 2017-06-07T17:37:11Z | |
| dc.date.available | 2017-06-07T17:37:11Z | |
| dc.date.created | 2016-05 | |
| dc.date.issued | 2016-01-15 | |
| dc.date.submitted | May 2016 | |
| dc.date.updated | 2017-06-07T17:37:11Z | |
| dc.description.abstract | When people utilize social applications and services, their privacy suffers potential serious threats. In this work, we present a novel, robust, and effective de-anonymization attack to mobility trace data and social network data. First, we design a Unified Similarity (US) measurement which takes into account local and global structural characteristics of data, information obtained from auxiliary data, and knowledge inherited from on-going de-anonymization results. By analyzing the measurement on real datasets, we find that some datasets can potentially be de-anonymized accurately and the others can be de-anonymized in a coarse granularity. Utilizing this property, we present a US based De-Anonymization (DA) frame-work, which iteratively de-anonymizes data with an accuracy guarantee. Then, to de-anonymize large scale data without the knowledge of the overlap size between the anonymized data and the auxiliary data, we generalize DA to an Adaptive De-Anonymization (ADA) framework. By strategically working on two core matching subgraphs, ADA achieves high de-anonymization accuracy and reduces computational overhead. Finally, we examine the presented de-anonymization attack on three well known mobility traces: St. Andrews, Infocom06, and Smallblue, and three social network datasets: ArnetMiner, Google+, and Facebook. The experimental results demonstrate that the presented de-anonymization framework is very effective and robust to noise. | |
| dc.description.degree | M.S. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1853/58160 | |
| dc.language.iso | en_US | |
| dc.publisher | Georgia Institute of Technology | |
| dc.subject | De-anonymization | |
| dc.subject | Social networks | |
| dc.title | De-anonymizing social networks and mobility traces | |
| dc.type | Text | |
| dc.type.genre | Thesis | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | School of Electrical and Computer Engineering | |
| local.contributor.corporatename | College of Engineering | |
| relation.isOrgUnitOfPublication | 5b7adef2-447c-4270-b9fc-846bd76f80f2 | |
| relation.isOrgUnitOfPublication | 7c022d60-21d5-497c-b552-95e489a06569 | |
| thesis.degree.level | Masters |