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.

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SNITCH: Leveraging IP Geolocation for Active VPN Detection

Tomer Schwartz (Data and Security Laboratory Fujitsu Research of Europe Ltd), Ofir Manor (Data and Security Laboratory Fujitsu Research of Europe Ltd), Andikan Otung (Data and Security Laboratory Fujitsu Research of Europe Ltd)

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DOITRUST: Dissecting On-chain Compromised Internet Domains via Graph Learning

Shuo Wang (CSIRO's Data61 & Cybersecurity CRC, Australia), Mahathir Almashor (CSIRO's Data61 & Cybersecurity CRC, Australia), Alsharif Abuadbba (CSIRO's Data61 & Cybersecurity CRC, Australia), Ruoxi Sun (CSIRO's Data61), Minhui Xue (CSIRO's Data61), Calvin Wang (CSIRO's Data61), Raj Gaire (CSIRO's Data61 & Cybersecurity CRC, Australia), Surya Nepal (CSIRO's Data61 & Cybersecurity CRC, Australia), Seyit Camtepe (CSIRO's…

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InfoMasker: Preventing Eavesdropping Using Phoneme-Based Noise

Peng Huang (Zhejiang University), Yao Wei (Zhejiang University), Peng Cheng (Zhejiang University), Zhongjie Ba (Zhejiang University), Li Lu (Zhejiang University), Feng Lin (Zhejiang University), Fan Zhang (Zhejiang University), Kui Ren (Zhejiang University)

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