Marina Moore, Aditya Sirish A Yelgundhalli (New York University), Justin Cappos (NYU)

Software supply chain attacks are a major concern and need to be addressed by every organization, including automakers. While there are many effective technologies in both the software delivery and broader software supply chain security space, combining these technologies presents challenges specific to automotive applications. We explore the trust boundaries between the software supply chain and software delivery systems to determine where verification of software supply chain metadata should occur, how to establish a root of trust, and how supply chain policy can be distributed. Using this exploration, we design Scudo, a secure combination of software over the air and software supply chain security technologies. We show that adding full verification of software supply chain metadata on-vehicle is not only inefficient, but is also largely unnecessary for security with multiple points of repository-side verification.

In addition, this paper describes a secure instantiation of Scudo, which integrates Uptane, a state of the art software update security solution, and in-toto, a comprehensive supply chain security framework. A practical deployment has shown that Scudo provides robust software supply chain protections. The client side power and processing costs are negligible, with the updated metadata comprising 0.504% of the total update transmission. The client side verification adds 0.21 seconds to the total update flow. This demonstrates that Scudo is easy to deploy in ways that can efficiently and effectively catch software supply chain attacks.

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SENSE: Enhancing Microarchitectural Awareness for TEEs via Subscription-Based Notification

Fan Sang (Georgia Institute of Technology), Jaehyuk Lee (Georgia Institute of Technology), Xiaokuan Zhang (George Mason University), Meng Xu (University of Waterloo), Scott Constable (Intel), Yuan Xiao (Intel), Michael Steiner (Intel), Mona Vij (Intel), Taesoo Kim (Georgia Institute of Technology)

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Differentially Private Dataset Condensation

Tianhang Zheng (University of Missouri-Kansas City), Baochun Li (University of Toronto)

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