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

in Proceedings of the 9th Conference on Availability, Reliability and Security (ARES 2014, Best Paper Award) (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 ▲]

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

Book published by EPTCS (2014)

Detailed reference viewed: 85 (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 detailA strand space approach to provable anonymity
Li, Yongjian; Pang, Jun UL

in Proc. 2nd Workshop on Formal Techniques for Safety-Critical Systems (FTSCS'13) (2014)

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See detailDynamic analysis of usage control policies.
Elrahaiby, Yehia; Pang, Jun UL

in Proceedings of the 11th Conference on Security and Cryptography (SECRPT) (2014)

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See detailModel-checking based approaches to parameter estimation of gene regulatory networks
Mizera, Andrzej UL; Pang, Jun UL; Yuan, Qixia UL

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

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See detailConstructing and comparing user mobility profiles
Chen, Xihui; Pang, Jun UL; Xue, Ran

in ACM Transactions on the Web (2014), 8(4), 21

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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 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 detailMeasuring User Similarity with Trajectory Patterns: Principles and New Metrics
Chen, Xihui; Lu, Ruipeng; Ma, Xiaoxing et al

in Proceedings of the 16th Asia-Pacific Web Conference (2014)

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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 detailModel Checking with Fairness Assumptions using PAT
Si, Yuanjie; Sun, Jun; Liu, Yang et al

in Frontiers of Computer Science (2014), 8(1), 1-16

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See detailDEMO: Demonstrating a Trust Framework for Evaluating GNSS Signal Integrity
Chen, Xihui UL; Harpes, Carlo; Lenzini, Gabriele UL et al

in Proceedings of 20th ACM Conference on Computer and Communications Security (CCS'13) (2013, November)

Through real-life experiments, it has been proved that spoofing is a practical threat to applications using the free civil service provided by Global Navigation Satellite Systems (GNSS). In this paper, we ... [more ▼]

Through real-life experiments, it has been proved that spoofing is a practical threat to applications using the free civil service provided by Global Navigation Satellite Systems (GNSS). In this paper, we demonstrate a prototype that can verify the integrity of GNSS civil signals. By integrity we intuitively mean that civil signals originate from a GNSS satellite without having been artificially interfered with. Our prototype provides interfaces that can incorporate existing spoofing detection methods whose results are then combined into an overall evaluation of the signal’s integrity, which we call integrity level. Considering the various security requirements from different applications, integrity levels can be calculated in many ways determined by their users. We also present an application scenario that deploys our prototype and offers a public central service – localisation assurance certification. Through experiments, we successfully show that our prototype is not only effective but also efficient in practice. [less ▲]

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See detailRecent development and biomedical applications of probabilistic Boolean networks
Trairatphisan, Panuwat UL; Mizera, Andrzej UL; Pang, Jun UL et al

in Cell Communication and Signaling (2013), 11(46),

Probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used for the study of the topology and dynamic aspects of biological systems. The combined use of rule-based ... [more ▼]

Probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used for the study of the topology and dynamic aspects of biological systems. The combined use of rule-based representation and probability makes PBN appealing for large-scale modelling of biological networks where degrees of uncertainty need to be considered. A considerable expansion of our knowledge in the field of theoretical research on PBN can be observed over the past few years, with a focus on network inference, network intervention and control. With respect to areas of applications, PBN is mainly used for the study of gene regulatory networks though with an increasing emergence in signal transduction, metabolic, and also physiological networks. At the same time, a number of computational tools, facilitating the modelling and analysis of PBNs, are continuously developed. A concise yet comprehensive review of the state-of-the-art on PBN modelling is offered in this article, including a comparative discussion on PBN versus similar models with respect to concepts and biomedical applications. Due to their many advantages, we consider PBN to stand as a suitable modelling framework for the description and analysis of complex biological systems, ranging from molecular to physiological levels. [less ▲]

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See detailExploring dependency for query privacy protection in location-based services
Chen, Xihui UL; Pang, Jun UL

in Proc. 3rd ACM Conference on Data and Application Security and Privacy (2013)

Detailed reference viewed: 96 (7 UL)
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See detailEditorial message: Special track on software verification and testing
Pang, Jun UL; Mousavi, Mohammad Reza

in Proc. 28th Symposium on Applied Computing (2013)

Detailed reference viewed: 31 (7 UL)
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See detailMathematical modelling of the Platelet-Derived Growth Factor (PDGF) signalling pathway
Mizera, Andrzej UL; Pang, Jun UL; Sauter, Thomas UL et al

in Proceedings of 4th Workshop on Computational Models for Cell Processes (CompMod'13) (2013)

Detailed reference viewed: 94 (10 UL)