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Ziqin Dang

dblp:348/3600 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2024
0009-0000-9732-7517ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 2 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Privacy and data protection · 61% Blockchain and cryptocurrency security · 39%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Privacy and data protection
anonymity
0.812024
Solution Probing Attack Against Coin Mixing Based Privacy-Preserving Crowdsourcing Platforms · IEEE Trans. Dependable Secur. Comput. 2024
Blockchain and cryptocurrency security › privacy-preserving payment
coin mixing
0.812024
Solution Probing Attack Against Coin Mixing Based Privacy-Preserving Crowdsourcing Platforms · IEEE Trans. Dependable Secur. Comput. 2024
Privacy and data protection › privacy-preserving computation
privacy-preserving crowdsourcing
0.812024
Solution Probing Attack Against Coin Mixing Based Privacy-Preserving Crowdsourcing Platforms · IEEE Trans. Dependable Secur. Comput. 2024

Methods — techniques the papers use, named apart from their topics

generative model · 0.8
YearPublicationVenuePosition
2024 Solution Probing Attack Against Coin Mixing Based Privacy-Preserving Crowdsourcing Platforms
abstract
Conventional crowdsourcing platforms primarily rely on a central server as the broker for information exchange. Although many efforts have been made, centralized platforms are still vulnerable to underlying security issues, such as an untrusted central server and single-point failure. Fortunately, blockchain has emerged as an alternative infrastructure for building crowdsourcing platforms. Many excellent designs of blockchain-based decentralized crowdsourcing (BDCS) solutions have been proposed. Benefiting from blockchain, BDCS can provide fascinating features, like tampering resistance and anonymity. However, a new attack surface appears in BDCS. Recently, a new attack against BDCS named solution probing attack has been identified. The solution-probing adversary can take advantage of the anonymity of BDCS to probe valid solutions using a generative model. Due to the transparency of blockchain transactions, the probing attack is effective even if solutions are encrypted. Nevertheless, we find transaction-mixing techniques effective in defending against probing attacks. In this paper, we introduce the solution probing attack and an improved variant, which can attack coin mixing-based BDCS. We evaluate probing attacks on large-scale crowdsourcing tasks. Experimental results show that the adversary is capable of deceiving BDCS with a limited number of probing, even if the BDCS is protected by solution encryption and coin mixing techniques.
Yunlong Mao, Ziqin Dang, Yuan Zhang 0004, Sheng Zhong 0002
IEEE Trans. Dependable Secur. Comput.2
2023 FLSwitch: Towards Secure and Fast Model Aggregation for Federated Deep Learning with a Learning State-Aware Switch
Yunlong Mao, Ziqin Dang, Tianling Zhang, Yuan Zhang 0004, Jingyu Hua, Sheng Zhong 0002
ACNS (1)2