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.

View More Papers

PowerRadio: Manipulate Sensor Measurement via Power GND Radiation

Yan Jiang (Zhejiang University), Xiaoyu Ji (Zhejiang University), Yancheng Jiang (Zhejiang University), Kai Wang (Zhejiang University), Chenren Xu (Peking University), Wenyuan Xu (Zhejiang University)

Read More

Non-intrusive and Unconstrained Keystroke Inference in VR Platforms via...

Tao Ni (City University of Hong Kong), Yuefeng Du (City University of Hong Kong), Qingchuan Zhao (City University of Hong Kong), Cong Wang (City University of Hong Kong)

Read More

LLMPirate: LLMs for Black-box Hardware IP Piracy

Vasudev Gohil (Texas A&M University), Matthew DeLorenzo (Texas A&M University), Veera Vishwa Achuta Sai Venkat Nallam (Texas A&M University), Joey See (Texas A&M University), Jeyavijayan Rajendran (Texas A&M University)

Read More

Passive Inference Attacks on Split Learning via Adversarial Regularization

Xiaochen Zhu (National University of Singapore & Massachusetts Institute of Technology), Xinjian Luo (National University of Singapore & Mohamed bin Zayed University of Artificial Intelligence), Yuncheng Wu (Renmin University of China), Yangfan Jiang (National University of Singapore), Xiaokui Xiao (National University of Singapore), Beng Chin Ooi (National University of Singapore)

Read More