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Significant amount of individual information are being collected and analyzed through a wide variety of applications across different industries. While pursuing better utility by discovering knowledge from the data, individuals’ 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.

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Abstract:

Significant amount of individual information are being collected and analyzed through a wide variety of applications across different industries. While pursuing better utility by discovering knowledge from the data, individuals’ 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 address this issue, the data typically goes under certain anonymization techniques, e.g., Property Preserving Encryption (PPE) or Differentially Private (DP). Unfortunately, most such techniques either are vulnerable to adversaries with prior knowledge, e.g., adversaries who fingerprint the network of a data owner, 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, 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:

  • The Multi-view approach which preserves both privacy and utility of data in network trace anonymization.
  • The R2DP Approach which is a novel technique on differentially private mechanism design with maximized utility.
  • The DPOAD Approach that is a novel framework on privacy preserving Anomaly detection in the outsourcing setting.

 

Biosketch:

Meisam is an active Research Scientist in CSIRO DATA61 which is the Australia’s leading digital research network, helping various partners across business, government and industry to solve a wide range of data-centric problems. Meisam’s research focuses on ethical machine learning (private and fair algorithm design), differential privacy, privacy preserving cloud security auditing and security issues pertaining to Internet of Things (IoT). Previously, he worked on the privacy preserving spatio-temporal monitoring project (Universite De Montreal) and the privacy preserving cloud computing and auditing in the Audit Ready Cloud (ARC) team (joint collaboration between the Ericsson Research Canada and the Concordia Institute for Information Systems Engineering). He also had several collaborations with the Department of Computer Science at the Illinois Institute of Technology (IIT).  Meisam has co-authored several papers in top-tier security journals and conferences. and his PhD dissertation has won the Distinguished PhD Dissertation Awards among all Engineering and Natural Science PhD dissertations and selected as Concordia University’s nominee for both Canada-wide CAGS and ADESAQ competitions.

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