Welcome to Hangzhou
|News! Selected papers of SocialSec 2015 will be invited to special issues in SCI-indexed journals including IEEE Transactions on Dependable and Secure Computing, ACM Transactions on Embedded Computing, Concurrency and Computation: Practice and Experience, and more. Check out all the journal special issues.|
Social Networks and Big Data have pervaded all aspects of our daily lives. With their unparalleled popularity, social networks have evolved from the platforms for social communication and news dissemination, to indispensable tools for professional networking, social recommendations, marketing, and online content distribution. Social Networks, together with other activities, produce Big Data that beyond the ability of commonly used computer software and hardware tools to capture, manage, and process within a tolerable elapsed time.
Due to their scale, complexity and heterogeneity, a number of technical and social challenges in Social Networks and Big Data must be addressed. It has been widely recognized that security and privacy are the critical issues. On one hand, Social Networks and Big Data have been the effective platform for the attackers to launch attacks and distribute malicious information. On the other hand, privacy leakage through Social Networks and Big Data has become common exercise. The aim of SocialSec 2015 is to provide a leading edge forum to foster interactions between researchers and developers with the security and privacy communities in Social Networks and Big Data, and to give attendees an opportunity to interact with experts in academia, industry, and governments.
|Submission Due||30 June 2015 (Extended)|
|Author Notification||10 August 2015|
|Registration and Camera-Ready Due||24 August 2015|
|Conference||16-18 November 2015|
Data-Driven Privacy Analysis
Professor Keith W. Ross
Dean of Engineering and Computer Science, NYU Shanghai
& Leonard J. Shustek Professor of Computer Science, CSE Dept, NYU
Abstract of Professor Keith Ross’s keynote speech will be posted soon.
Short Bio: Keith Ross is the Dean of Engineering and Computer Science at NYU Shanghai and the Leonard J. Shustek Chair Professor in the Computer Science and Engineering Dept at NYU. His current research interests are in data-driven analysis of online social networks and privacy. He has also worked on peer-to-peer networking, Internet measurement, video streaming, and Markov decision processes. He is co-author (with James F. Kurose) of the popular textbook, Computer Networking: A Top-Down Approach Featuring the Internet, published by Pearson (first edition in 2000, sixth edition 2012). It is the most popular textbook on computer networking, both nationally and internationally, and has been translated into fourteen languages. He was a co-founder of the company Wimba, which developed voice and video technologies for online learning and was acquired by Blackboard in 2010. He is an ACM Fellow and an IEEE Fellow.
Identifying Propagation Sources in Social Networks
Professor Wanlei Zhou
Alfred Deakin Professor, Chair of Information Technology,
School of Information Technology,
Deakin University, Melbourne, Australia
It has long been a significant but difficult problem to identify propagation sources based on limited knowledge of network structures and the varying states of network nodes. Real cases of identifying propagation sources include finding the spreader of malware in computer networks, locating the sources of rumours in online social networks and finding origins of a rolling blackout in smart grids. This talk reviews the state-of-the art in source identification techniques, esp. the application of these techniques in social networks, and discusses the pros and cons of current methods in this field. In order to gain a quantitative understanding of current methods, we provide a series of experiments and comparisons based on various environment settings. Our experiments reveal considerable differences in performance by employing different network topologies, various propagation schemes and diverse propagation probabilities. We then present our work in modelling the propagation of worms and rumours in social networks, as well as our work in dealing with multi-source identification of information diffusion. This talk is mainly be based on our recently published papers:
1. Sheng Wen, Wei Zhou, Jun Zhang, Yang Xiang, Wanlei Zhou, and Weijia Jia, “Modeling Propagation Dynamics of Social Network Worms”, IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 8, pp. 1633-1643, Aug. 2013.
2. Yini Wang, Sheng Wen, Yang Xiang, and Wanlei Zhou, “Modeling the Propagation of Worms in Networks: A Survey”, IEEE Communications Surveys and Tutorials, Volume:16, Issue:2, pp 942-960, 2014.
3. Sheng Wen, Wei Zhou, Jun Zhang, Yang Xiang, Wanlei Zhou, Weijia Jia, and Cliff C.Zou “Modeling and Analysis on the Propagation Dynamics of Modern Email Malware”, IEEE Transactions on Dependable and Secure Computing, VOL. 11, NO. 4, pp. 361-374, JULY/AUGUST 2014.
4. Sheng Wen, Jiaojiao Jiang, Yang Xiang, Shui Yu, and Wanlei Zhou, “Are the Popular Users Always Important for the Information Dissemination in Online Social Networks?” IEEE Network, pp. 64-67, September/October 2014.
5. Sheng Wen, Jiaojiao Jiang, Yang Xiang, Shui Yu, Wanlei Zhou, Weijia Jia, ?To Shut Them Up or to Clarify: Restraining the Spread of Rumours in Online Social Networks?, IEEE Transactions on Parallel and Distributed Systems, vol. 25, No. 12, pp. 3306-3316, 2014.
6. Sheng Wen, Mohammad Sayad Haghighi, Chao Chen, Yang Xiang, Wanlei Zhou, and Weijia Jia, “A Sword with Two Edges: Propagation Studies on Both Positive and Negative Information in Online Social Networks”, IEEE Transactions on Computers, Vol. 64, No. 3, pp. 640-653, March 2015.
7. Jiaojiao Jiang, Sheng Wen, Shui Yu, Yang Xiang, Wanlei Zhou, and Ekram Hossain, “Identifying Propagation Sources in Networks: State-of-the-Art and Comparative Studies”, Accepted by IEEE Communications Surveys and Tutorials, accepted 17/9/2014.
Short Bio: Professor Wanlei Zhou received the B.Eng and M.Eng degrees from Harbin Institute of Technology, Harbin, China in 1982 and 1984, respectively, and the PhD degree from The Australian National University, Canberra, Australia, in 1991, all in Computer Science and Engineering. He also received a DSc degree (a higher Doctorate degree) from Deakin University in 2002. He is currently the Alfred Deakin Professor (the highest honour the University can bestow on a member of academic staff) and Chair Professor in Information Technology, School of Information Technology, Deakin University. Professor Zhou has been the Head of School of Information Technology twice (Jan 2002-Apr 2006 and Jan 2009-Jan 2015) and Associate Dean of Faculty of Science and Technology in Deakin University (May 2006-Dec 2008). Before joining Deakin University, Professor Zhou served as a lecturer in University of Electronic Science and Technology of China, a system programmer in HP at Massachusetts, USA; a lecturer in Monash University, Melbourne, Australia; and a lecturer in National University of Singapore, Singapore. His research interests include distributed systems, network security, bioinformatics, and e-learning. Professor Zhou has published more than 300 papers in refereed international journals and refereed international conferences proceedings. He has also chaired many international conferences. Prof Zhou is a Senior Member of the IEEE.
Differential Privacy Techniques for Correlated Dataset Half-day Event
Professor Wanlei Zhou, Deakin University, Australia
Dr Tianqing Zhu, Deakin University, Australia
Differential privacy is one of the most prevalent privacy models in privacy preserving research area. As a rigorous and provable privacy model, differential privacy helps to construct diverse privacy preserving frameworks. However, previous work has mainly focused on independent datasets that assumes all records were sampled from a universe independently. A real-world dataset often exhibits strong coupling relations: some records are often correlated with each other, and this may disclose extra information than expected. We categorize these real-world data into static coupled dataset and dynamic coupled dataset. The former dataset will not change during the privacy preserving process and the latter dataset will continual changed in period. In the tutorial, we will provide an overview of the differential privacy and explore the application of differential privacy techniques in both static coupled and dynamic coupled dataset.
This tutorial is divided into two part. The first part will review the scope of differential privacy, including differential privacy data release and differential privacy data analysis. The second part will discuss the application of differential privacy techniques in coupled environment, which contains static coupled dataset and dynamic coupled stream. We will show that coupled differential privacy creates new opportunities, directions and means for privacy preserving in real world scenarios. This tutorial is for those interested in gaining a better understanding of the privacy preserving and beginning to apply differential privacy in real world applications.
The conference seeks submissions from academia, industry, and government presenting novel research on all theoretical and practical aspects of security and privacy in Social Networks and Big Data. Papers describing case studies, implementation experiences, and lessons learned are also encouraged. Topics of interest include but are not limited to:
- Attacks in/via Social Networks
- Information control and detection
- Malicious behavior modeling in Social Networks
- Malicious information propagation via Social Networks
- Phishing problems in Social Networks
- Privacy protection in Social Networks
- Big Data analytics for threats and attacks prediction
- Spam problems in Social Networks
- Trust and reputations in Social Networks
- Big Data outsourcing
- Big Data forensics
- Big SociaData
- Security and privacy in Big Database
- Applied Cryptography for Big Data
- Big Data system security
- Mobile Social Networks security
- Security and privacy in cloud
- Forensics in Social Networks and Big Data
Please click the above button to submit your paper through SocialSec 2015 Easychair submission system. Submitted papers must not substantially overlap with papers that have been published or that are simultaneously submitted to a journal or a conference with proceedings. Papers must be clearly presented in English, must not exceed 8 pages in A4 format, including tables, figures, references and appendixes, in IEEE Computer Society proceedings format with Portable Document Format (.pdf). Please refer to IEEE Manuscript Templates for Conference Proceedings for preparing the submission.
Papers will be selected based on their originality, timeliness, significance, relevance, and clarity of presentation. Submission of a paper should be regarded as a commitment that, should the paper be accepted, at least one of the authors will register and attend the conference to present the work.
Journal Special Issues
Selected papers of SocialSec 2015 will be invited to the following journal special issues:
|IEEE Transactions on Dependable and Secure Computing Impact Factor: 1.137
Special Issue on Social Network Security
|ACM Transactions on Embedded Computing Impact Factor: 0.68
Special Issue on Embedded Device Forensics and Security: State of the Art Advances
|Concurrency and Computation: Practice and Experience Impact Factor: 0.784
Special Issue on Security and Privacy in Social Networks
|Security and Communication Networks Impact Factor: 0.433
Special Issue on Advance in Secure Data Storage and Computation in Cloud (pending)
Jifeng He, East China Normal University, China
Yang Xiang, Deakin University, Australia
Program Committee Chairs
Wenzhi Chen, Zhejiang University, China
Elisa Bertino, Purdue University, USA
Xinyi Huang, Fujian Normal University, China
Program Committee Members
Gail-Joon Ahn, Arizona State University, USA
Cristina Alcaraz, University of Malaga, Spain
Man Ho Au, Hong Kong Polytechnic University
Joonsang Baek, KUSTAR, UAE
Filipe Beato, University of Leuven, Belgium
Ero Balsa, University of Leuven, Belgium
Barbara Carminati, University of Insubria, Italy
David Chadwick, University of Kent, UK
Richard Chbeir, CNRS, France
Xiaofeng Chen, Xidian University, China
Reza Curtmola, New Jersey Institute of Technology, USA
Yong Ding, Guilin University of Electronic Technology, China
Steven Furnell, Plymouth University, UK
Alban Gabillon, University of Polynésie Française, French Polynesia
Joaquin Garcia-Alfaro, TELECOM Bretagne, France
Gabriel Ghinita, University of Massachusetts at Boston, USA
Jin Han, Twitter, US
Murat Kantarcioglu, University of Texas at Dallas, US
Sokratis Katsikas, University of Piraeus, Greece
Stefan Katzenbeisser, TU Darmstadt, Germany
Muhammad Khurram Khan , King Saud University, Kingdom of Saudi Arabia
Shinsaku Kiyomoto , KDDI R&D; Laboratories Inc., Japan
Ashish Kundu, IBM Thomas J. Watson Research Center
Jinguang Han, Nanjing University of Finance & Economics , China
Alejandro Hevia, Universidad de Chile, Chile
Costas Lambrinoudakis, University of Piraeus, Greece
Adam J. Lee, University of Pittsburgh, USA
Joseph K. Liu, Institute for Infocomm Research, Singapore
William Liu, Auckland University of Technology, New Zealand
Rongxing Lu, Nanyang Technological University, Singapore
Carlos Maziero, Federal Technological University, Brazil
Wojciech Mazurczyk, Warsaw University of Technology, Poland
Surya Nepal, CSIRO, Australia
Jaehong Park , Eastern Michigan University, USA
Gerardo Pelosi, Politecnico di Milano, Italy
Günther Pernul, University of Regensburg, Germany
Thorsten Strufe, , Dresden University of Technology, Germany
Chunhua Su, JAIST, Japan
Hung-Min Sun, National Tsing Hua University, Taiwan
Pedro García-Teodoro, University of Granada, Spain
Lingyu Wang, Concordia University, Canada
Qian Wang, Wuhan University, China
Yu Wang, Deakin University, Australia
Sheng Wen, Deakin University, Australia
Qianhong Wu, Beihang Univerisity, China
Guomin Yang, University of Wollongong, Australia
Tsz Hon Yuen, Huawei, Singapore
Yong Yu, University of Electronic Science and Technology of China, China
Zhenfeng Zhang, Institute of Software, Chinese Academy of Sciences
Mingwu Zhang, Hubei University of Technology, China
For further information regarding to the conference, please send email to firstname.lastname@example.org.