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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Oksana Kulyk (ITU Copenhagen), Willard Rafnsson (IT University of Copenhagen), Ida Marie Borberg, Rene Hougard Pedersen

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Mohammed Lamine Bouchouia (Telecom Paris - Institut Polytechnique de Paris), Jean-Philippe Monteuuis (Qualcomm), Houda Labiod (Telecom Paris - Institut Polytechnique de Paris), Ons Jelassi, Wafa Ben Jaballah (Thales) and Jonathan Petit (Telecom Paris - Institut Polytechnique de Paris)

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MacOS versus Microsoft Windows: A Study on the Cybersecurity...

Cem Topcuoglu (Northeastern University), Andrea Martinez (Florida International University), Abbas Acar (Florida International University), Selcuk Uluagac (Florida International University), Engin Kirda (Northeastern University)

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Context-Sensitive and Directional Concurrency Fuzzing for Data-Race Detection

Zu-Ming Jiang (Tsinghua University), Jia-Ju Bai (Tsinghua University), Kangjie Lu (University of Minnesota), Shi-Min Hu (Tsinghua University)

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