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This content will become publicly available on June 29, 2026

Title: Security and Privacy of Integrated Sensing and Communication Systems
Integrated sensing and communication (ISAC) is considered an emerging technology for 6th-generation (6G) wireless and mobile networks. It is expected to enable a wide variety of vertical applications, ranging from unmanned aerial vehicles (UAVs) detection for critical infrastructure protection to physiological sensing for mobile healthcare. Despite its significant socioeconomic benefits, ISAC technology also raises unique challenges in system security and user privacy. Being aware of the security and privacy challenges, understanding the trade-off between security and communication performance, and exploring potential countermeasures in practical systems are critical to a wide adoption of this technology in various application scenarios. This talk will discuss various security and privacy threats in emerging ISAC systems with a focus on communication-centric ISAC systems, that is, using the cellular or WiFi infrastructure for sensing. We will then examine potential mechanisms to secure ISAC systems and protect user privacy at the physical and data layers under different sensing modes. At the wireless physical (PHY) layer, an ISAC system is subject to both passive and active attacks, such as unauthorized passive sensing, unauthorized active sensing, signal spoofing, and jamming. Potential countermeasures include wireless channel/radio frequency (RF) environment obfuscation, waveform randomization, anti-jamming communication, and spectrum/RF monitoring. At the data layer, user privacy could be compromised during data collection, sharing, storage, and usage. For sensing systems powered by artificial intelligence (AI), user privacy could also be compromised during the model training and inference stages. An attacker could falsify the sensing data to achieve a malicious goal. Potential countermeasures include the application of privacy enhancing technologies (PETs), such as data anonymization, differential privacy, homomorphic encryption, trusted execution, and data synthesis.  more » « less
Award ID(s):
2318796
PAR ID:
10621642
Author(s) / Creator(s):
Publisher / Repository:
ACM
Date Published:
ISBN:
9798400715310
Page Range / eLocation ID:
1 to 1
Format(s):
Medium: X
Location:
Arlington VA USA
Sponsoring Org:
National Science Foundation
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