Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Luca Favaro (Technical University of Munich), and Florian Matthes (Technical University of Munich)

Privacy-Enhancing Technologies (PETs) have gained considerable attention in the past decades, particularly in academia but also in practical settings. The proliferation of promising technologies from research presents only one perspective, and the true success of PETs should also be measured in their adoption in the industry. Yet, a potential issue arises with the very terminology of Privacy-Enhancing Technology: what exactly is a PET, and what is not? To tackle this question, we begin with the academic side, investigating various definitions of PETs proposed in the literature over the past 30 years. Next, we compare our findings with the awareness and understanding of PETs in practice by conducting 20 semi-structured interviews with privacy professionals. Additionally, we conduct two surveys with 67 total participants, quantifying which of the technologies from the literature practitioners consider to be PETs, while also evaluating new definitions that we propose. Our results show that there is little agreement in academia and practice on how the term Privacy-Enhancing Technologies is understood. We conclude that there is much work to be done towards facilitating a common understanding of PETs and their transition from research to practice.

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IsolateGPT: An Execution Isolation Architecture for LLM-Based Agentic Systems

Yuhao Wu (Washington University in St. Louis), Franziska Roesner (University of Washington), Tadayoshi Kohno (University of Washington), Ning Zhang (Washington University in St. Louis), Umar Iqbal (Washington University in St. Louis)

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Securing BGP ASAP: ASPA and other Post-ROV Defenses

Justin Furuness (University of Connecticut), Cameron Morris (University of Connecticut), Reynaldo Morillo (University of Connecticut), Arvind Kasiliya (University of Connecticut), Bing Wang (University of Connecticut), Amir Herzberg (University of Connecticut)

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Defending Against Membership Inference Attacks on Iteratively Pruned Deep...

Jing Shang (Beijing Jiaotong University), Jian Wang (Beijing Jiaotong University), Kailun Wang (Beijing Jiaotong University), Jiqiang Liu (Beijing Jiaotong University), Nan Jiang (Beijing University of Technology), Md Armanuzzaman (Northeastern University), Ziming Zhao (Northeastern University)

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