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 Connected Vehicle User Privacy -- A SAFE PROJECT


In the new wave of IoT, automotive industry is experiencing the advent of unprecedented applications with the connected devices. To enable such applications, a plethora of multi-modal data collected from the vehicles and brought-in mobile devices need to be provided to third parties (e.g., insurance firms, car sharing businesses, healthcare providers, clinical and transportation forums), hence enabling next generation data driven and connected apps/services. Meanwhile, privacy is a critical factor behind customer adoption. The consequence in the case of loss of privacy is expected to be disastrous, as witnessed bymany business cases over the last decade. The high multi-modality of collected data and the dynamic data aggregation by different players make the design of cloud-connected vehicle much more challenging. Hence, it is important to understand the privacy implication in the emerging connected vehicle ecosystem and develop practical methods forachieving both user privacy and data ubiquity.

The goal of this project is to create a holistic platform that securely collects data from multiple sources (e.g., vehiclesand brought-in devices) and integrates them in the cloud to enable next-generation connected apps and services with guaranteed user privacy protection. To achieve this goal, we propose to:

  • Characterize multi-modal datafrom their privacy impactstandpoint, i.e., understand the privacy implications of each data item individually as well as all possible combinatorial datasets that could be generated.
  • Develop resource efficient and privacy preserving data uploadpolicies, i.e., depending on the use case/context, there will be different data upload policies that optimize privacy requirements and resource consumption (e.g., processing, network bandwidth).
  • Implement a dynamic querying frameworkto enable a broad range of end user services with access to unprecedented multi-modal information and guaranteed user privacy protection.
This project is a Ford-University of Michigan Alliance Project.


People
   
   

Professors: Di Ma, Brahim Medjahed

Students: Huaxin Li

 
Publications
   
   

  1. H. Li, D. Ma, B. Medjahed, Q. Wang, YS. Kim, P. Mitra, "Secure and Privacy-Preserving Data Collection Mechanisms for Connected Vehicles", WCX™ 17: SAE World Congress Experience, January 2017.  [PDF]

 
     

Last update: 9/25/2017. All rights reserved.
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