References of "Pang, Jun 50002807"
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See detailCommunity-Driven Social Influence Analysis and Applications
Zhang, Yang UL; Pang, Jun UL

in Proceedings of the 15th International Conference on Web Engineering (2015)

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See detailA new access control scheme for Facebook-style social networks
Pang, Jun UL; Zhang, Yang UL

in Computers & Security (2015), 54

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See detailActivity tracking: A new attack on location privacy
Chen, Xihui; Mizera, Andrzej UL; Pang, Jun UL

in Proceedings of the 3rd IEEE Conference on Communications and Network Security (CNS'15) (2015)

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See detailA logical approach to restricting access in online social networks
Cramer, Marcos UL; Pang, Jun UL; Zhang, Yang UL

in Proceedings of the 20th ACM Symposium on Access Control Models and Technologies (2015)

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See detailLocation prediction: Communities speak louder than friends
Pang, Jun UL; Zhang, Yang UL

in Proceedings of the 3rd ACM Conference on Online Social Networks (COSN'15) (2015)

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See detailFormalizing provable anonymity in Isabelle/HOL
Li, Yongjian; Pang, Jun UL

in Formal Aspects of Computing (2015), 27(2), 255-282

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See detailProceedings 4th International Workshop on Engineering Safety and Security Systems
Pang, Jun UL; Liu, Yang; Mauw, Sjouke UL

Book published by EPTCS - 184 (2015)

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See detailCryptographic protocols for enforcing relationship-based access control policies
Pang, Jun UL; Zhang, Yang UL

in Proceedings of the 39th Annual IEEE Computers, Software & Applications Conference (COMPSAC'15) (2015)

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See detailInferring friendship from check-in data of location-based social networks
Cheng, Ran; Pang, Jun UL; Zhang, Yang UL

in Proceedings of the 7th International Conference on Advances in Social Networks Analysis and Mining (ASONAM'15) (2015)

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See detailEvent prediction with community leaders
Pang, Jun UL; Zhang, Yang UL

in Proceedings of the 10th International Conference on Availability, Reliability and Security (ARES'15) (2015)

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See detailExploring communities for effective location prediction
Pang, Jun UL; Zhang, Yang UL

in Proceedings of the 24th World Wide Web Conference (2015)

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See detailASSA-PBN: An approximate steady-state analyser for probabilistic Boolean networks
Mizera, Andrzej UL; Pang, Jun UL; Yuan, Qixia UL

in Proceedings of the 13th International Symposium on Automated Technology for Verification and Analysis (ATVA'15) (2015)

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See detailoptPBN: An Optimisation Toolbox for Probabilistic Boolean Networks
Trairatphisan, Panuwat UL; Mizera, Andrzej UL; Pang, Jun UL et al

in PLoS ONE (2014), 9(7), 980011-15

Background There exist several computational tools which allow for the optimisation and inference of biological networks using a Boolean formalism. Nevertheless, the results from such tools yield only ... [more ▼]

Background There exist several computational tools which allow for the optimisation and inference of biological networks using a Boolean formalism. Nevertheless, the results from such tools yield only limited quantitative insights into the complexity of biological systems because of the inherited qualitative nature of Boolean networks. Results We introduce optPBN, a Matlab-based toolbox for the optimisation of probabilistic Boolean networks (PBN) which operates under the framework of the BN/PBN toolbox. optPBN offers an easy generation of probabilistic Boolean networks from rule-based Boolean model specification and it allows for flexible measurement data integration from multiple experiments. Subsequently, optPBN generates integrated optimisation problems which can be solved by various optimisers. In term of functionalities, optPBN allows for the construction of a probabilistic Boolean network from a given set of potential constitutive Boolean networks by optimising the selection probabilities for these networks so that the resulting PBN fits experimental data. Furthermore, the optPBN pipeline can also be operated on large-scale computational platforms to solve complex optimisation problems. Apart from exemplary case studies which we correctly inferred the original network, we also successfully applied optPBN to study a large-scale Boolean model of apoptosis where it allows identifying the inverse correlation between UVB irradiation, NFκB and Caspase 3 activations, and apoptosis in primary hepatocytes quantitatively. Also, the results from optPBN help elucidating the relevancy of crosstalk interactions in the apoptotic network. Summary The optPBN toolbox provides a simple yet comprehensive pipeline for integrated optimisation problem generation in the PBN formalism that can readily be solved by various optimisers on local or grid-based computational platforms. optPBN can be further applied to various biological studies such as the inference of gene regulatory networks or the identification of the interaction's relevancy in signal transduction networks. [less ▲]

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See detailSymbolic analysis of an electric vehicle charging protocol.
Li, Li; Pang, Jun UL; Liu, Yang et al

in Proceedings of 19th IEEE Conference on Engineering of Complex Computer Systems (ICECCS) (2014)

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See detailProceedings of the 16th International Conference on Formal Engineering Methods
Merz, Stephan; Pang, Jun UL

Book published by Springer (2014)

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See detailFoundational aspects of security
Chatzikokolakis, Konstantinos; Mödersheim, Sebastian; Palamidessi, Catuscia et al

in Journal of Computer Security (2014), 22(2), 201-202

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See detailProceedings Third International Workshop on Engineering Safety and Security Systems
Pang, Jun UL; Liu, Yang

Book published by EPTCS (2014)

Detailed reference viewed: 92 (1 UL)
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See detailSpecial issue on software verification and testing (editorial message)
Mousavi, MohammadReza; Pang, Jun UL

in Science of Computer Programming (2014), 95(3), 273-274

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See detailMinUS: Mining User Similarity with Trajectory Patterns
Chen, Xihui; Kordy, Piotr; Lu, Ruipeng et al

in Proceedings of 17th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) (2014)

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See detailProtecting query privacy in location-based services
Chen, Xihui UL; Pang, Jun UL

in GeoInformatica (2014), 18(1), 95-133

The popularity of location-based services (LBSs) leads to severe concerns on users’ privacy. With the fast growth of Internet applications such as online social networks, more user information becomes ... [more ▼]

The popularity of location-based services (LBSs) leads to severe concerns on users’ privacy. With the fast growth of Internet applications such as online social networks, more user information becomes available to the attackers, which allows them to construct new contextual information. This gives rise to new challenges for user privacy protection and often requires improvements on the existing privacy-preserving methods. In this paper, we classify contextual information related to LBS query privacy and focus on two types of contexts – user profiles and query dependency: user profiles have not been deeply studied in LBS query privacy protection, while we are the first to show the impact of query dependency on users’ query privacy. More specifically, we present a general framework to enable the attackers to compute a distribution on users with respect to issuing an observed request. The framework can model attackers with different contextual information. We take user profiles and query dependency as examples to illustrate the implementation of the framework and their impact on users’ query privacy. Our framework subsequently allows us to show the insufficiency of existing query privacy metrics, e.g., k-anonymity, and propose several new metrics. In the end, we develop new generalisation algorithms to compute regions satisfying users’ privacy requirements expressed in these metrics. By experiments, our metrics and algorithms are shown to be effective and efficient for practical usage. [less ▲]

Detailed reference viewed: 151 (9 UL)