Parinya Ekparinya (University of Sydney), Vincent Gramoli (University of Sydney and CSIRO-Data61), Guillaume Jourjon (CSIRO-Data61)

The vulnerability of traditional blockchains have been demonstrated at multiple occasions. Various companies are now moving towards Proof-of-Authority (PoA) blockchains with more conventional Byzantine fault tolerance, where a known set of n permissioned sealers, among which no more than t are Byzantine, seal blocks that include user transactions. Despite their wide adoption, these protocols were not proved correct.

In this paper, we present the Cloning Attack against the two mostly deployed PoA implementations of Ethereum, namely Aura and Clique. The Cloning Attack consists of one sealer cloning its pair of public-private keys into two distinct Ethereum instances that communicate with distinct groups of sealers. To identify their vulnerabilities, we first specify the corresponding algorithms. We then deploy one testnet for each protocol and demonstrate the success of the attack with only one Byzantine sealer. Finally, we propose counter-measures that prevent an adversary from double spending and introduce the necessary number of sealers needed to decide a block depending on n and t for both Aura and Clique to be safe.

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CloudLeak: Large-Scale Deep Learning Models Stealing Through Adversarial Examples

Honggang Yu (University of Florida), Kaichen Yang (University of Florida), Teng Zhang (University of Central Florida), Yun-Yun Tsai (National Tsing Hua University), Tsung-Yi Ho (National Tsing Hua University), Yier Jin (University of Florida)

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Qiben Yan (Michigan State University), Kehai Liu (Chinese Academy of Sciences), Qin Zhou (University of Nebraska-Lincoln), Hanqing Guo (Michigan State University), Ning Zhang (Washington University in St. Louis)

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Zhongjie Wang (University of California, Riverside), Shitong Zhu (University of California, Riverside), Yue Cao (University of California, Riverside), Zhiyun Qian (University of California, Riverside), Chengyu Song (University of California, Riverside), Srikanth V. Krishnamurthy (University of California, Riverside), Kevin S. Chan (U.S. Army Research Lab), Tracy D. Braun (U.S. Army Research Lab)

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Locally Differentially Private Frequency Estimation with Consistency

Tianhao Wang (Purdue University), Milan Lopuhaä-Zwakenberg (Eindhoven University of Technology), Zitao Li (Purdue University), Boris Skoric (Eindhoven University of Technology), Ninghui Li (Purdue University)

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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)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

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

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

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