Privacy Preservation in IoT-based Cyber-Physical Systems
Nowadays, we find ourselves surrounded by several IoT-based Cyber-Physical Systems (CPS) that track our activities and collect sensitive information about us. While such systems promise to ease our lives, they raise major privacy concerns for their users. Existing approaches for privacy protection give users only a passive role in protecting their privacy. However, recent studies show that users are becoming more conscious of their privacy, and tend to take a pragmatic stance on sharing their private data.
My Ph.D. project aims to provide a novel solution that gives users an active role in protecting their privacy by allowing them to identify what can be inferred about them when they share personal information, quantify privacy risks that they become subject to, and control accordingly the extent of the sharing.
The main goals of this project can be summarized as follows:
- Analyze diverse IoT data types and determine the inference's certainty for each data collected item
- Propose formal models that assist users in taking their data sharing decisions based on various forms of privacy risks and benefits
- Build-up flexible obfuscation techniques that allow the implementation of fine-grained privacy decisions
- Privacy Engineering
- Internet of Things (IoT)
- IoT Privacy
- Context-aware Computing
- Semantic Modeling
- Semantic Reasoning
- Big Data
The LIUPPA (laboratory) T2I team
LIUPPA is a computer science laboratory related to the “Université de PAU et des Pays de l’Adour” (UPPA), and interested in the formalization of digital ecosystems in multimedia sensory networks and microgrids. The lab is present in the various campuses of the university in southern France, particulary in Anglet, Mont-de-Marsan, and Pau.
The lab contains two teams: T2I and MOVIES each dedicated to different research activities.
- SUEM: An Ontology for Semantic User Environment Modeling
- Privacy Oracle: Context-aware System for Dynamic Privacy Risk Inference
- Karam Bou Chaaya, Mahmoud Barhamgi, Richard Chbeir, Philippe Arnould, Djamal Benslimane. Context-aware System for Dynamic Privacy Risk Inference. Future Generation Computer Systems, Elsevier, 2019, 101, pp.1096-1111. ⟨10.1016/j.future.2019.07.011⟩.