Secure computation allows for computation of arbitrary functions directly on encrypted data and hides all information about the data against untrusted parties, even if the untrusted parties are involved in the computation. Secure computation could enable, for example, companies to outsource and process their data in untrusted clouds, or two mutually untrusted parties to mine their datasets together. However, secure computation currently is often too inefficient to be practical. Despite recent huge improvements, secure computation is still tens of thousand to billions times slower than computation in the clear. This becomes a major impediment to widespread use of secure computation.
I started my research on efficient secure computation in 2011. I tackle this problem from complete new angles, by designing cryptographic data structures and associated protocols that allow more efficient and scalable secure computation and by building efficient secure computation protocols using recently developed cryptographic primitives. The protocols have been motivated by and applied in solving security/private issues in cloud computing, data mining, and machine learning.
Related publications
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Secret-Shared Shuffle with Malicious Security
Xiangfu Song, Dong Yin, Jianli Bai,
Changyu Dong, and Ee-Chien Chang
In 31st Annual Network and Distributed System Security Symposium (NDSS 2024), 2024
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Predicate Private Set Intersection with Linear Complexity
Yaxi Yang, Jian Weng, Yufeng Yi,
Changyu Dong, Leo Yu Zhang, and Jianying Zhou
In 21st International Conference on Applied Cryptography and Network Security
(ACNS 2023), 2023
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Multi-party Updatable Delegated Private Set Intersection
Aydin Abadi,
Changyu Dong, Steven Murdoch, and Sotirios Terzis
In 26th International Conference on Financial Cryptography and Data Security, 2022
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How to Make Private Distributed Cardinality Estimation Practical,
and Get Differential Privacy for Free
Changhui Hu, Jin Li, Zheli Liu, Xiaojie Guo, Yu Wei, Xuan Guang, Grigorios Loukides, and Changyu Dong
In 30th USENIX Security Symposium, 2021
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TKDE
MAS-Encryption and Its Applications in Privacy-Preserving Classifiers
Chongzhi Gao, Jin Li, Shibing Xia, Kim-Kwang Raymond Choo, Wenjing Lou, and Changyu Dong
IEEE Trans. Knowl. Data Eng., 2020
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TDSC
Efficient Delegated Private Set Intersection on Outsourced Private
Datasets
Aydin Abadi, Sotirios Terzis, Roberto Metere, and Changyu Dong
IEEE Trans. Dependable Secur. Comput., 2019
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TIFS
Approximating Private Set Union/Intersection Cardinality With Logarithmic
Complexity
Changyu Dong, and Grigorios Loukides
IEEE Trans. Inf. Forensics Secur., 2017
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VD-PSI: Verifiable Delegated Private Set Intersection on Outsourced
Private Datasets
Aydin Abadi, Sotirios Terzis, and Changyu Dong
In 20th International Conference on Financial Cryptography and Data Security, 2016
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O-PSI: Delegated Private Set Intersection on Outsourced Datasets
Aydin Abadi, Sotirios Terzis, and Changyu Dong
In 30th IFIP TC 11
International Conference on ICT Systems Security and Privacy Protection, 2015
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Efficient Data Intensive Secure Computation: Fictional or Real?
Changyu Dong
In 23rd International Workshop on Security Protocols, 2015
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A Fast Single Server Private Information Retrieval Protocol with Low
Communication Cost
Changyu Dong, and Liqun Chen
In 19th European Symposium on Research
in Computer Security, 2014
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A Fast Secure Dot Product Protocol with Application to Privacy Preserving
Association Rule Mining
Changyu Dong, and Liqun Chen
In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2014
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Efficient protocols for private record linkage
Zikai Wen, and Changyu Dong
In ACM Symposium on Applied Computing, 2014
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When private set intersection meets big data: an efficient and scalable
protocol
Changyu Dong, Liqun Chen, and Zikai Wen
In ACM SIGSAC Conference on Computer and Communications Security, 2013
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Fair Private Set Intersection with a Semi-trusted Arbiter
Changyu Dong, Liqun Chen, Jan Camenisch, and Giovanni Russello
In 27th Annual IFIP
WG 11.3 Conference on Data and Applications Security and Privacy, 2013