Scott Jordan (University of California, Irvine), Yoshimichi Nakatsuka (University of California, Irvine), Ercan Ozturk (University of California, Irvine), Andrew Paverd (Microsoft Research), Gene Tsudik (University of California, Irvine)

Recent data protection regulations (notably, GDPR and CCPA) grant consumers various rights, including the right to access, modify or delete any personal information collected about them (and retained) by a service provider. To exercise these rights, one must submit a verifiable consumer request proving that the collected data indeed pertains to them. This action is straightforward for consumers with active accounts with a service provider at the time of data collection, since they can use standard (e.g., password-based) means of authentication to validate their requests. However, a major conundrum arises from the need to support consumers without accounts to exercise their rights. To this end, some service providers began requiring such accountless consumers to reveal and prove their identities (e.g., using government-issued documents, utility bills, or credit card numbers) as part of issuing a verifiable consumer request. While understandable as a short-term fix, this approach is cumbersome and expensive for service providers as well as privacy-invasive for consumers.

Consequently, there is a strong need to provide better means of authenticating requests from accountless consumers. To achieve this, we propose VICEROY, a privacy-preserving and scalable framework for producing proofs of data ownership, which form a basis for verifiable consumer requests. Building upon existing web techniques and features, VICEROY allows accountless consumers to interact with service providers, and later prove that they are the same person in a privacy-preserving manner, while requiring minimal changes for both parties. We design and implement VICEROY with emphasis on security/privacy, deployability and usability. We also assess its practicality via extensive experiments.

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PPA: Preference Profiling Attack Against Federated Learning

Chunyi Zhou (Nanjing University of Science and Technology), Yansong Gao (Nanjing University of Science and Technology), Anmin Fu (Nanjing University of Science and Technology), Kai Chen (Chinese Academy of Science), Zhiyang Dai (Nanjing University of Science and Technology), Zhi Zhang (CSIRO's Data61), Minhui Xue (CSIRO's Data61), Yuqing Zhang (University of Chinese Academy of Science)

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Kids, Cats, and Control: Designing Privacy and Security Dashboard...

Jacob Abbott (Indiana University), Jayati Dev (Indiana University), DongInn Kim (Indiana University), Shakthidhar Reddy Gopavaram (Indiana University), Meera Iyer (Indiana University), Shivani Sadam (Indiana University) , Shirang Mare (Western Washington University), Tatiana Ringenberg (Purdue University), Vafa Andalibi (Indiana University), and L. Jean Camp(Indiana University)

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Sometimes, You Aren’t What You Do: Mimicry Attacks against...

Akul Goyal (University of Illinois at Urbana-Champaign), Xueyuan Han (Wake Forest University), Gang Wang (University of Illinois at Urbana-Champaign), Adam Bates (University of Illinois at Urbana-Champaign)

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dewolf: Improving Decompilation by leveraging User Surveys

Steffen Enders, Eva-Maria C. Behner, Niklas Bergmann, Mariia Rybalka, Elmar Padilla (Fraunhofer FKIE, Germany), Er Xue Hui, Henry Low, Nicholas Sim (DSO National Laboratories, Singapore)

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