Harry Halpin (Nym Technologies)

With the ascendance of artificial intelligence (AI), one of the largest problems facing privacy-enhancing technologies (PETs) is how they can successfully counter-act the large-scale surveillance that is required for the collection of data–and metadata–necessary for the training of AI models. While there has been a flurry of research into the foundations of AI, the field of privacy-enhancing technologies still appears to be a grabbag of techniques without an overarching theoretical foundation. However, we will point to the potential unification of AI and PETS via the concepts of signal and noise, as formalized by informationtheoretic metrics like entropy. We overview the concept of entropy (“noise”) and its applications in both AI and PETs. For example, mixnets can be thought of as noise-generating networks, and so the inverse of neural networks. Then we defend the use of entropy as a metric to compare both different PETs, as well as both PETs and AI systems.

View More Papers

Automatic Insecurity: Exploring Email Auto-configuration in the Wild

Shushang Wen (School of Cyber Science and Technology, University of Science and Technology of China), Yiming Zhang (Tsinghua University), Yuxiang Shen (School of Cyber Science and Technology, University of Science and Technology of China), Bingyu Li (School of Cyber Science and Technology, Beihang University), Haixin Duan (Tsinghua University; Zhongguancun Laboratory), Jingqiang Lin (School of Cyber…

Read More

A New PPML Paradigm for Quantized Models

Tianpei Lu (The State Key Laboratory of Blockchain and Data Security, Zhejiang University), Bingsheng Zhang (The State Key Laboratory of Blockchain and Data Security, Zhejiang University), Xiaoyuan Zhang (The State Key Laboratory of Blockchain and Data Security, Zhejiang University), Kui Ren (The State Key Laboratory of Blockchain and Data Security, Zhejiang University)

Read More

Siniel: Distributed Privacy-Preserving zkSNARK

Yunbo Yang (The State Key Laboratory of Blockchain and Data Security, Zhejiang University), Yuejia Cheng (Shanghai DeCareer Consulting Co., Ltd), Kailun Wang (Beijing Jiaotong University), Xiaoguo Li (College of Computer Science, Chongqing University), Jianfei Sun (School of Computing and Information Systems, Singapore Management University), Jiachen Shen (Shanghai Key Laboratory of Trustworthy Computing, East China Normal…

Read More

LeakLess: Selective Data Protection against Memory Leakage Attacks for...

Maryam Rostamipoor (Stony Brook University), Seyedhamed Ghavamnia (University of Connecticut), Michalis Polychronakis (Stony Brook University)

Read More