Security and Privacy in Machine Learning and CPS

Mobile devices and cyber-physical systems (CPS) are equipped with numerous sensors which allow them to offer efficient and effective personalized services and applications. For example, connected and autonomous vehicles (CAVs) feature advanced sensing capabilities, including multiples of range sensors (Lidar and Radar), 360° cameras, onboard GPUs, and high-speed connectivity: Tesla Motors uses a forward radar, a front-facing camera, and multiple ultrasonic sensors to enable its Autopilot feature; Google’s and Apple’s version of CAV uses Lidar and cameras to support autonomous driving; Ford and Uber are also actively experimenting with CAVs.

These advanced capabilities open up a plethora of exciting opportunities for next generation services related to better localization and navigation and traffic optimization. At the same time, their reliance on sensing data and machine learning algorithms for route prediction, collision avoidance and object detection and recognitions, introduces new attack surfaces. Given the widening gap between autonomy and security in this application domain, in tandem with their safety repercussions, there is an impending need for novel solutions that can guarantee trusted outcomes from such sensor-fusion and machine learning algorithms.

 Recent Publications:

  • [MAISP @ MobiSys] Temporal Consistency Checks to Detect LiDAR Spoofing Attacks in Autonomous Vehicle Perception. Chengzeng You, Zhongyuan Hau, Soteris Demetriou. To appear in the 1st Workshop on Security and Privacy for Mobile AI (MAISP’21), co-located with ACM MobiSys 2021
  • [DPML @ ICLR] Layer-wise Characterization of Latent Information Leakage in Federated Learning. Fan Mo, Anastasia Borovykh, Mohammad Malekzadeh, Hamed Haddadi, Soteris Demetriou. Distributed and Private Machine Learning (DPML) workshop, co-located with ICLR, 2021
  • [ESORICS] Shadow-Catcher: Looking Into Shadows to Detect Ghost Objects in Autonomous Vehicle 3D Sensing. Zhongyuan Hau, Soteris Demetriou, Luis Muñoz-González, Emil C. Lupu. 26th European Symposium on Research in Computer Security (ESORICS), 2021
  • [AutoSec @ NDSS] Object Removal Attacks on LiDAR-based 3D Object Detectors. Zhongyuan Hau, Kenneth T Co, Soteris Demetriou, Emil C Lupu. 3rd International Workshop  on Automotive and Autonomous Vehicle Security (AutoSec), co-located with NDSS, 2021.  (Runner-up for best short paper award).
  • [MobiSys] DarkneTZ: Towards Model Privacy on the Edge using Trusted Execution Environments. Fan Mo, Ali Shahin, Kleomenis Katevas, Soteris Demetriou, Ilias Leontiadis, Andrea Cavallaro, Hamed Haddadi. In proceedings of the 18th ACM international conference on Mobile systems, applications, and services (ACM MobiSys), 2020
  • [US Patent] Determining Car Positions. Puneet Jain, Soteris Demetriou, Kyu-Han Kim, US 10380889 B2, 2019
  • [INFOCOM] CoDrive: Improving Automobile Positioning via Collaborative DrivingDemetriou, Soteris; Jain, Puneet; Kim, Kyu-Han. IEEE International Conference on Computer Communications (IEEE INFOCOM), April 2018 (Best in Session Presentation Award @ INFOCOM)
  • [SenSys] CamForensics: Understanding Visual Privacy Leaks in the Wild. Srivastava, Animesh; Jain, Puneet; Demetriou, Soteris; Cox, Landon; Kim, Kyu-Han. 15th ACM Conference on Embedded Networked Sensor Systems (ACM SenSys), November 2017
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