ORCID Profile
0000-0001-5621-767X
Current Organisations
RMIT University
,
Victoria University
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Publisher: ACM
Date: 28-11-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Publisher: Springer International Publishing
Date: 2017
Publisher: Wiley
Date: 05-09-2019
DOI: 10.1002/CPE.4706
Publisher: Springer Nature Switzerland
Date: 2022
Publisher: MDPI AG
Date: 05-09-2022
DOI: 10.3390/INFORMATICS9030066
Abstract: An important direction of informatics is devoted to the protection of privacy of confidential information while providing answers to aggregated queries that can be used for analysis of data. Protecting privacy is especially important when aggregated queries are used to combine personal information stored in several databases that belong to different owners or come from different sources. Malicious attackers may be able to infer confidential information even from aggregated numerical values returned as answers to queries over large collections of data. Formal proofs of security guarantees are important, because they can be used for implementing practical systems protecting privacy and providing answers to aggregated queries. The investigation of formal conditions which guarantee protection of private information against inference attacks originates from a fundamental result obtained by Chin and Ozsoyoglu in 1982 for linear queries. The present paper solves similar problems for two new classes of aggregated nonlinear queries. We obtain complete descriptions of conditions, which guarantee the protection of privacy of confidential information against certain possible inference attacks, if a collection of queries of this type are answered. Rigorous formal security proofs are given which guarantee that the conditions obtained ensure the preservation of privacy of confidential data. In addition, we give necessary and sufficient conditions for the protection of confidential information from special inference attacks aimed at achieving a group compromise.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 15-11-2022
DOI: 10.1007/S11276-022-03130-6
Abstract: Recommender systems have been widely used for implementing personalised content on many mobile online services to reduce computational overload and preserve wireless data for users. The underlying mechanisms used for building recommender systems analyse data collected from users to make recommendations. This poses concerns over the privacy of data from users as both service providers and the cloud will have access. Privacy-preserving recommender systems protect user information by incorporating various cryptographic mechanisms to prevent accessing the data. However, existing works are not practical due to the use of heavy cryptography. In this paper, we propose an efficient privacy-preserving recommender system that takes advantage of clustering to improve efficiency. Using a secure clustering mechanism, user data are assigned to multiple clusters before being fed into the recommendation. Our proposed protocols are privacy-preserving and do not leak information that could be used to identify a data subject. The experiments show that our system is efficient and accurate.
Publisher: Springer International Publishing
Date: 2021
Publisher: Elsevier BV
Date: 10-2020
Publisher: Springer International Publishing
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
Publisher: ACM
Date: 04-02-2020
Publisher: Elsevier BV
Date: 2019
Publisher: Springer International Publishing
Date: 2022
Publisher: Springer International Publishing
Date: 2016
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 2019
Publisher: Springer International Publishing
Date: 2022
Publisher: IEEE
Date: 10-2021
Publisher: Elsevier BV
Date: 11-2020
Publisher: Springer Science and Business Media LLC
Date: 14-09-2022
DOI: 10.1007/S11276-022-03108-4
Abstract: Privacy-enhancing techniques and protocols for data aggregation and analytics in wireless networks require the development of novel methods for efficient and privacy-preserving computation of distributed queries with the protection of outcomes from active attackers. Previous approaches to secure privacy-preserving computation of distributed queries incur significant communication overhead and cannot be applied to big data. This paper proposes two solutions to the problem of efficient and privacy-preserving computation of distributed queries with the protection of outcomes from active outsider attackers for a new large class of distributed statistical or numerical queries. This class contains many useful statistics and is larger than other classes considered in the literature previously. We propose two protocols for the Protection of data from Active Attackers (PAA) in the case of distributed privacy-preserving computation: PAA applying Shamir’s Secret Sharing (PAA-SSS) and PAA applying homomorphic encryption (PAA-HE). The PAA-HE protocol combines the use of ElGamal and Paillier encryption schemes in one system. Theoretical analysis and experimental results show that both protocols outperform alternative approaches. PAA-HE provides stronger protection and is more efficient than PAA-SSS.
Publisher: ACM
Date: 30-01-2023
Publisher: ACM
Date: 08-12-2014
Publisher: Springer International Publishing
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Elsevier BV
Date: 11-2017
Publisher: Elsevier BV
Date: 06-2019
No related grants have been discovered for Xuechao Yang.