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See detailTo Share or not to Share: Access Control and Information Inference in Social Networks
Zhang, Yang UL

Doctoral thesis (2016)

Online social networks (OSNs) have been the most successful online applications during the past decade. Leading players in the business, including Facebook, Twitter and Instagram, attract a huge number of ... [more ▼]

Online social networks (OSNs) have been the most successful online applications during the past decade. Leading players in the business, including Facebook, Twitter and Instagram, attract a huge number of users. Nowadays, OSNs have become a primary way for people to connect, communicate and share life moments. Although OSNs have brought a lot of convenience to our life, users' privacy, on the other hand, has become a major concern due to the large amount of personal data shared online. In this thesis, we study users' privacy in social networks from two aspects, namely access control and information inference. Access control is a mechanism, provided by OSNs, for users themselves to regulate who can view their resources. Access control schemes in OSNs are relationship-based, i.e., a user can define access control policies to allow others who are in a certain relationship with him to access his resources. Current OSNs have deployed multiple access control schemes, however most of these schemes do not satisfy users' expectations, due to expressiveness and usability. There are mainly two types of information that users share in OSNs, namely their activities and social relations. The information has provided an unprecedented chance for academia to understand human society and for industry to build appealing applications, such as personalized recommendation. However, the large quantity of data can also be used to infer a user's personal information, even though not shared by the user in OSNs. This thesis concentrates on users' privacy in online social networks from two aspects, i.e., access control and information inference, it is organized into two parts. The first part of this thesis addresses access control in social networks from three perspectives. First, we propose a formal framework based on a hybrid logic to model users' access control policies. This framework incorporates the notion of public information and provides users with a fine-grained way to control who can view their resources. Second, we design cryptographic protocols to enforce access control policies in OSNs. Under these protocols, a user can allow others to view his resources without leaking private information. Third, major OSN companies have deployed blacklist for users to enforce extra access control besides the normal access control policies. We formally model blacklist with the help of a hybrid logic and propose efficient algorithms to implement it in OSNs. The second part of this thesis concentrates on the inference of users' information in OSNs, using machine learning techniques. The targets of our inference are users' activities, represented by mobility, and social relations. First, we propose a method which uses a user's social relations to predict his locations. This method adopts a user's social community information to construct the location predictor, and perform the inference with machine learning techniques. Second, we focus on inferring the friendship between two users based on the common locations they have been to. We propose a notion namely location sociality that characterizes to which extent a location is suitable for conducting social activities, and use this notion for friendship prediction. Experiments on real life social network datasets have demonstrated the effectiveness of our two inferences. [less ▲]

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See detailAn Empirical Study on User Access Control in Online Social Networks
Ni, Minyue; Zhang, Yang UL; Han, Weili et al

in Proceedings of the 21st ACM Symposium on Access Control Models and Technologies (SACMAT'16) (2016)

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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 detailPharmacological characterization of an antisense knockdown zebrafish model of Dravet syndrome: Inhibition of epileptic seizures by the serotonin agonist fenfluramine
Zhang, Yang UL; Kecskés, A.; Copmans, D. et al

in PLoS ONE (2015), 10(5),

Dravet syndrome (DS) is one of the most pharmacoresistant and devastating forms of childhood epilepsy syndromes. Distinct de novo mutations in the SCN1A gene are responsible for over 80% of DS cases ... [more ▼]

Dravet syndrome (DS) is one of the most pharmacoresistant and devastating forms of childhood epilepsy syndromes. Distinct de novo mutations in the SCN1A gene are responsible for over 80% of DS cases. While DS is largely resistant to treatment with existing anti-epileptic drugs, promising results have been obtained in clinical trials with human patients treated with the serotonin agonist fenfluramine as an add-on therapeutic. We developed a zebrafish model of DS using morpholino antisense oligomers (MOs) targeting scn1Lab, the zebrafish ortholog of SCN1A. Zebrafish larvae with an antisense knockdown of scn1Lab (scn1Lab morphants) were characterized by automated behavioral tracking and high-resolution video imaging, in addition to measuring brain activity through local field potential recordings. Our findings reveal that scn1Lab morphants display hyperactivity, convulsive seizure-like behavior, loss of posture, repetitive jerking and a myoclonic seizure-like pattern. The occurrence of spontaneous seizures was confirmed by local field potential recordings of the forebrain, measuring epileptiform discharges. Furthermore, we show that these larvae are remarkably sensitive to hyperthermia, similar to what has been described for mouse models of DS, as well as for human DS patients. Pharmacological evaluation revealed that sodium valproate and fenfluramine significantly reduce epileptiform discharges in scn1Lab morphants. Our findings for this zebrafish model of DS are in accordance with clinical data for human DS patients. To our knowledge, this is the first study demonstrating effective seizure inhibition of fenfluramine in an animal model of Dravet syndrome. Moreover, these results provide a basis for identifying novel analogs with improved activity and significantly milder or no side effects. © 2015 Zhang et al. [less ▲]

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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 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 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 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 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 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 detailTwisted Edwards-Form Elliptic Curve Cryptography for 8-bit AVR-based Sensor Nodes
Chu, Dalin; Groszschädl, Johann UL; Liu, Zhe UL et al

in Chen, Kefei; Xie, Qi; Qiu, Weidong (Eds.) et al Proceedings of the first ACM Workshop on Asia Public-Key Cryptography (ASIAPKC 2013) (2013, May)

Wireless Sensor Networks (WSNs) pose a number of unique security challenges that demand innovation in several areas including the design of cryptographic primitives and protocols. Despite recent progress ... [more ▼]

Wireless Sensor Networks (WSNs) pose a number of unique security challenges that demand innovation in several areas including the design of cryptographic primitives and protocols. Despite recent progress, the efficient implementation of Elliptic Curve Cryptography (ECC) for WSNs is still a very active research topic and techniques to further reduce the time and energy cost of ECC are eagerly sought. This paper presents an optimized ECC implementation that we developed from scratch to comply with the severe resource constraints of 8-bit sensor nodes such as the MICAz and IRIS motes. Our ECC software uses Optimal Prime Fields (OPFs) as underlying algebraic structure and supports two different families of elliptic curves, namely Weierstraß-form and twisted Edwards-form curves. Due to the combination of efficient field arithmetic and fast group operations, we achieve an execution time of 5.8*10^6 clock cycles for a full 158-bit scalar multiplication on an 8-bit ATmega128 microcontroller, which is 2.78 times faster than the widely-used TinyECC library. Our implementation also shows that the energy cost of scalar multiplication on a MICAz (or IRIS) mote amounts to just 19 mJ when using a twisted Edwards curve over a 160-bit OPF. This result compares fairly well with the energy figures of two recently-presented hardware designs of ECC based on twisted Edwards curves. [less ▲]

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