PhD Oral Exam - Meisam Mohammady, Information and Systems Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

Significant amount of individual information is being collected and analyzed today through a wide variety of applications across different industries. While pursuing better utility by discovering knowledge from the data, an individual’s privacy may be compromised during an analysis: corporate networks monitor their online behavior; advertising companies collect and share their private information, and cybercriminals cause financial damages through security breaches. To this end, the data typically goes under certain anonymization techniques, e.g., CryptoPAn [Computer Networks’ 04], which replaces real IP addresses with prefix-preserving pseudonyms, or Differentially Private (DP) [ICALP’06] techniques which modify the answer to a query by adding a zero-mean noise distributed according to, e.g., a Laplace distribution. Unfortunately, most such techniques either are vulnerable to adversaries with prior knowledge, e.g., some network flows in the data, or require heavy data sanitization or perturbation, both of which may result in a significant loss of data utility. Therefore, the fundamental trade-off between privacy and utility (i.e., analysis accuracy) has attracted significant attention in various settings [ICALP’06, ACM CCS’14]. In line with this track of research, in this dissertation we aim to build utility-maximized and privacy-preserving tools for Internet communications. Such tools can be employed not only by dissidents and whistleblowers, but also, by ordinary Internet users on a daily basis. To this end, we combine the development of practical systems with rigorous theoretical analysis, and incorporate techniques from various disciplines such as computer networking, cryptography, and statistical analysis. During the research, we proposed three different frameworks in some well-known settings outlined in the following.

First, we propose the Multi-view Approach to preserve both privacy and utility in network trace anonymization, second, the R^2 DP Approach which is a novel technique on differentially private mechanism design with maximized utility, and third, the DPOD Approach that is a novel framework on privacy preserving anomaly detection in the outsourcing setting.