Kosei Akama (Keio University), Yoshimichi Nakatsuka (ETH Zurich), Masaaki Sato (Tokai University), Keisuke Uehara (Keio University)

Preventing abusive activities caused by adversaries accessing online services at a rate exceeding that expected by websites has become an ever-increasing problem. CAPTCHAs and SMS authentication are widely used to provide a solution by implementing rate limiting, although they are becoming less effective, and some are considered privacy-invasive. In light of this, many studies have proposed better rate-limiting systems that protect the privacy of legitimate users while blocking malicious actors. However, they suffer from one or more shortcomings: (1) assume trust in the underlying hardware and (2) are vulnerable to side-channel attacks.
Motivated by the aforementioned issues, this paper proposes Scrappy: SeCure Rate Assuring Protocol with PrivacY. Scrappy allows clients to generate unforgeable yet unlinkable rate-assuring proofs, which provides the server with cryptographic guarantees that the client is not misbehaving. We design Scrappy using a combination of DAA and hardware security devices. Scrappy is implemented over three types of devices, including one that can immediately be deployed in the real world. Our baseline evaluation shows that the end-to-end latency of Scrappy is minimal, taking only 0.32 seconds, and uses only 679 bytes of bandwidth when transferring necessary data. We also conduct an extensive security evaluation, showing that the rate-limiting capability of Scrappy is unaffected even if the hardware security device is compromised.

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Hongsheng Hu (CSIRO's Data61), Shuo Wang (CSIRO's Data61), Jiamin Chang (University of New South Wales), Haonan Zhong (University of New South Wales), Ruoxi Sun (CSIRO's Data61), Shuang Hao (University of Texas at Dallas), Haojin Zhu (Shanghai Jiao Tong University), Minhui Xue (CSIRO's Data61)

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Arjun Arunasalam (Purdue University), Habiba Farrukh (University of California, Irvine), Eliz Tekcan (Purdue University), Z. Berkay Celik (Purdue University)

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Sourav Das (University of Illinois at Urbana-Champaign), Zhuolun Xiang (Aptos), Ling Ren (University of Illinois at Urbana-Champaign)

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SigmaDiff: Semantics-Aware Deep Graph Matching for Pseudocode Diffing

Lian Gao (University of California Riverside), Yu Qu (University of California Riverside), Sheng Yu (University of California, Riverside & Deepbits Technology Inc.), Yue Duan (Singapore Management University), Heng Yin (University of California, Riverside & Deepbits Technology Inc.)

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Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

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Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

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Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)