Secure and Privacy-Preserving Data Collection Mechanisms for Connected Vehicles University of Michigan-Dearborn

Format:

Conference/Event

Author/Creator:

Li, Li, author.

Contributor:

Kim, Yu Seung

Ma, Di

Medjahed, Brahim

Mitra, Pramita

Wang, Chian Yi

Conference Name:

WCX 17: SAE World Congress Experience (2017-04-04 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2017

Summary:

Nowadays, the automotive industry is experiencing the advent of unprecedented applications with connected devices, such as identifying safe users for insurance companies or assessing vehicle health. To enable such applications, driving behavior data are collected from vehicles and provided to third parties (e.g., insurance firms, car sharing businesses, healthcare providers). In the new wave of IoT (Internet of Things), driving statistics and users' data generated from wearable devices can be exploited to better assess driving behaviors and construct driver models. We propose a framework for securely collecting data from multiple sources (e.g., vehicles and brought-in devices) and integrating them in the cloud to enable next-generation services with guaranteed user privacy protection. To achieve this goal, we design fine-grained privacy-aware data collection and upload policies that balance between enforcing privacy requirements and optimizing resource consumption (e.g., processing, network bandwidth). The optimal policy will be determined by the privacy index of the integrated multi-source data to be used by the specific service and the desired resource usage. Real-world experiments and privacy leakage analysis are conducted to address privacy issues in vehicle data collection and integration, raise public awareness around privacy leakage, and validate the proposed system

Notes:

Vendor supplied data

Publisher Number:

2017-01-1660

Access Restriction:

Restricted for use by site license