Web privacy measurement has often focused on the implementation specifics of various tracking techniques, developing ways to block them, and producing browser add-ons which demonstrate such blocking. However, while over 20 years of this focus has yielded lots of papers, citations, and media coverage, there has been limited real-world impact. A much more promising approach to effecting systemic change at scale is to shift attention away from how tracking is performed towards evaluating if such tracking is compliant with a growing body of applicable regulations.

In this talk I will offer perspectives on compliance measurement at scale, drawing lessons from my experience in the worlds of academic research, civil liberties advocacy, class litigation, and industry. Common themes will be explored and large-scale compliance measurement technologies will be presented in-depth. Likewise, insights on how computer scientists may effectively work across and between disciplinary boundaries will be presented. Ultimately, the most effective means to achieve change at scale is not to build another add-on, it is to build coalitions of experts working together to ensure technology, business, and regulation exist in harmony.

View More Papers

MetaWave: Attacking mmWave Sensing with Meta-material-enhanced Tags

Xingyu Chen (University of Colorado Denver), Zhengxiong Li (University of Colorado Denver), Baicheng Chen (University of California San Diego), Yi Zhu (SUNY at Buffalo), Chris Xiaoxuan Lu (University of Edinburgh), Zhengyu Peng (Aptiv), Feng Lin (Zhejiang University), Wenyao Xu (SUNY Buffalo), Kui Ren (Zhejiang University), Chunming Qiao (SUNY at Buffalo)

Read More

BrowserFM: A Feature Model-based Approach to Browser Fingerprint Analysis

Maxime Huyghe (Univ. Lille, Inria, CNRS, UMR 9189 CRIStAL), Clément Quinton (Univ. Lille, Inria, CNRS, UMR 9189 CRIStAL), Walter Rudametkin (Univ. Rennes, Inria, CNRS, UMR 6074 IRISA)

Read More

SoundLock: A Novel User Authentication Scheme for VR Devices...

Huadi Zhu (The University of Texas at Arlington), Mingyan Xiao (The University of Texas at Arlington), Demoria Sherman (The University of Texas at Arlington), Ming Li (The University of Texas at Arlington)

Read More

Fusion: Efficient and Secure Inference Resilient to Malicious Servers

Caiqin Dong (Jinan University), Jian Weng (Jinan University), Jia-Nan Liu (Jinan University), Yue Zhang (Jinan University), Yao Tong (Guangzhou Fongwell Data Limited Company), Anjia Yang (Jinan University), Yudan Cheng (Jinan University), Shun Hu (Jinan University)

Read More