Visible to the public Biblio

Filters: Author is Wang, Jian  [Clear All Filters]
2020-09-28
Han, Xu, Liu, Yanheng, Wang, Jian.  2018.  Modeling and analyzing privacy-awareness social behavior network. IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :7–12.
The increasingly networked human society requires that human beings have a clear understanding and control over the structure, nature and behavior of various social networks. There is a tendency towards privacy in the study of network evolutions because privacy disclosure behavior in the network has gradually developed into a serious concern. For this purpose, we extended information theory and proposed a brand-new concept about so-called “habitual privacy” to quantitatively analyze privacy exposure behavior and facilitate privacy computation. We emphasized that habitual privacy is an inherent property of the user and is correlated with their habitual behaviors. The widely approved driving force in recent modeling complex networks is originated from activity. Thus, we propose the privacy-driven model through synthetically considering the activity impact and habitual privacy underlying the decision process. Privacy-driven model facilitates to more accurately capture highly dynamical network behaviors and figure out the complex evolution process, allowing a profound understanding of the evolution of network driven by privacy.
2020-08-13
Zhou, Kexin, Wang, Jian.  2019.  Trajectory Protection Scheme Based on Fog Computing and K-anonymity in IoT. 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS). :1—6.
With the development of cloud computing technology in the Internet of Things (IoT), the trajectory privacy in location-based services (LBSs) has attracted much attention. Most of the existing work adopts point-to-point and centralized models, which will bring a heavy burden to the user and cause performance bottlenecks. Moreover, previous schemes did not consider both online and offline trajectory protection and ignored some hidden background information. Therefore, in this paper, we design a trajectory protection scheme based on fog computing and k-anonymity for real-time trajectory privacy protection in continuous queries and offline trajectory data protection in trajectory publication. Fog computing provides the user with local storage and mobility to ensure physical control, and k-anonymity constructs the cloaking region for each snapshot in terms of time-dependent query probability and transition probability. In this way, two k-anonymity-based dummy generation algorithms are proposed, which achieve the maximum entropy of online and offline trajectory protection. Security analysis and simulation results indicate that our scheme can realize trajectory protection effectively and efficiently.
2020-05-15
Wang, Jian, Guo, Shize, Chen, Zhe, Zhang, Tao.  2019.  A Benchmark Suite of Hardware Trojans for On-Chip Networks. IEEE Access. 7:102002—102009.
As recently studied, network-on-chip (NoC) suffers growing threats from hardware trojans (HTs), leading to performance degradation or information leakage when it provides communication service in many/multi-core systems. Therefore, defense techniques against NoC HTs experience rapid development in recent years. However, to the best of our knowledge, there are few standard benchmarks developed for the defense techniques evaluation. To address this issue, in this paper, we design a suite of benchmarks which involves multiple NoCs with different HTs, so that researchers can compare various HT defense methods fairly by making use of them. We first briefly introduce the features of target NoC and its infected modules in our benchmarks, and then, detail the design of our NoC HTs in a one-by-one manner. Finally, we evaluate our benchmarks through extensive simulations and report the circuit cost of NoC HTs in terms of area and power consumption, as well as their effects on NoC performance. Besides, comprehensive experiments, including functional testing and side channel analysis are performed to assess the stealthiness of our HTs.
Lian, Mengyun, Wang, Jian, Lu, Jinzhi.  2018.  A New Hardware Logic Circuit for Evaluating Multi-Processor Chip Security. 2018 Eighth International Conference on Instrumentation Measurement, Computer, Communication and Control (IMCCC). :1571—1574.
NoC (Network-on-Chip) is widely considered and researched by academic communities as a new inter-core interconnection method that replaces the bus. Nowadays, the complexity of on-chip systems is increasing, requiring better communication performance and scalability. Therefore, the optimization of communication performance has become one of the research hotspots. While the NoC is rapidly developing, it is threatened by hardware Trojans inserted during the design or manufacturing processes. This leads to that the attackers can exploit NoC's vulnerability to attack the on-chip systems. To solve the problem, we design and implement a replay-type hardware Trojan inserted into the NoC, aiming to provide a benchmark test set to promote the defense strategies for NoC hardware security. The experiment proves that the power consumption of the designed Trojan accounts for less than one thousandth of the entire NoC power consumption and area. Besides, simulation experiments reveal that this replaytype hardware Trojan can reduce the network throughput.