Simin Ghesmati (Uni Wien, SBA Research), Walid Fdhila (Uni Wien, SBA Research), Edgar Weippl (Uni Wien, SBA Research)

Over the past years, the interest in Blockchain technology and its applications has tremendously increased. This increase of interest was however accompanied by serious threats that raised concerns over user data privacy. Prominent examples include transaction traceability and identification of senders, receivers, and transaction amounts. This resulted in a multitude of privacy-preserving techniques that offer different guarantees in terms of trust, decentralization, and traceability. CoinJoin [22] is one of the promising techniques that adopts a decentralized approach to achieve privacy on the Unspent Transaction Output (UTXO) based blockchain. Despite the advantages of such a technique in obfuscating user transaction data, making them usable to common users requires considerable development and integration efforts. This paper provides a comprehensive usability study of three main Bitcoin wallets that integrate the CoinJoin technique, i.e., Joinmarket, Wasabi, and Samourai. A cognitive walkthrough based on usability and design criteria was conducted in order to evaluate the ease of use of these wallets. The study findings will enable privacy wallet developers to gain valuable insights into a better user experience.

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Detecting Obfuscated Function Clones in Binaries using Machine Learning

Michael Pucher (University of Vienna), Christian Kudera (SBA Research), Georg Merzdovnik (SBA Research)

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hbACSS: How to Robustly Share Many Secrets

Thomas Yurek (University of Illinois at Urbana-Champaign), Licheng Luo (University of Illinois at Urbana-Champaign), Jaiden Fairoze (University of California, Berkeley), Aniket Kate (Purdue University), Andrew Miller (University of Illinois at Urbana-Champaign)

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DRIVETRUTH: Automated Autonomous Driving Dataset Generation for Security Applications

Raymond Muller (Purdue University), Yanmao Man (University of Arizona), Z. Berkay Celik (Purdue University), Ming Li (University of Arizona) and Ryan Gerdes (Virginia Tech)

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MIRROR: Model Inversion for Deep LearningNetwork with High Fidelity

Shengwei An (Purdue University), Guanhong Tao (Purdue University), Qiuling Xu (Purdue University), Yingqi Liu (Purdue University), Guangyu Shen (Purdue University); Yuan Yao (Nanjing University), Jingwei Xu (Nanjing University), Xiangyu Zhang (Purdue University)

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