Visible to the public Biblio

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Yu, Wei, Zhou, Yuanyuan, Zhou, Xuejun, Wang, Lei, Chen, Shang.  2020.  Study on Statistical Analysis Method of Decoy-state Quantum Key Distribution with Finite-length Data. 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 1:2435—2440.
In order to solve the statistical fluctuation problem caused by the finite data length in the practical quantum key distribution system, four commonly used statistical methods, DeMoivre-Laplace theorem, Chebyshev inequality, Chernoff boundary and Hoeffding boundary, are used to analyze. The application conditions of each method are discussed, and the effects of data length and confidence level on quantum key distribution security performance are simulated and analyzed. The simulation results show that the applicable conditions of Chernoff boundary are most consistent with the reality of the practical quantum key distribution system with finite-length data. Under the same experimental conditions, the secure key generation rate and secure transmission distance obtained by Chernoff boundary are better than those of the other three methods. When the data length and confidence level change, the stability of the security performance obtained by the Chernoff boundary is the best.
Wang, Lei, Manchester, Ian R., Trumpf, Jochen, Shi, Guodong.  2020.  Initial-Value Privacy of Linear Dynamical Systems. 2020 59th IEEE Conference on Decision and Control (CDC). :3108—3113.
This paper studies initial-value privacy problems of linear dynamical systems. We consider a standard linear time-invariant system with random process and measurement noises. For such a system, eavesdroppers having access to system output trajectories may infer the system initial states, leading to initial-value privacy risks. When a finite number of output trajectories are eavesdropped, we consider a requirement that any guess about the initial values can be plausibly denied. When an infinite number of output trajectories are eavesdropped, we consider a requirement that the initial values should not be uniquely recoverable. In view of these two privacy requirements, we define differential initial-value privacy and intrinsic initial-value privacy, respectively, for the system as metrics of privacy risks. First of all, we prove that the intrinsic initial-value privacy is equivalent to unobservability, while the differential initial-value privacy can be achieved for a privacy budget depending on an extended observability matrix of the system and the covariance of the noises. Next, the inherent network nature of the considered linear system is explored, where each individual state corresponds to a node and the state and output matrices induce interaction and sensing graphs, leading to a network system. Under this network system perspective, we allow the initial states at some nodes to be public, and investigate the resulting intrinsic initial- value privacy of each individual node. We establish necessary and sufficient conditions for such individual node initial-value privacy, and also prove that the intrinsic initial-value privacy of individual nodes is generically determined by the network structure.
Li, Tao, Ren, Yongzhen, Ren, Yongjun, Wang, Lina, Wang, Lingyun, Wang, Lei.  2019.  NMF-Based Privacy-Preserving Collaborative Filtering on Cloud Computing. 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). :476–481.
The security of user personal information on cloud computing is an important issue for the recommendation system. In order to provide high quality recommendation services, privacy of user is often obtained by untrusted recommendation systems. At the same time, malicious attacks often use the recommendation results to try to guess the private data of user. This paper proposes a hybrid algorithm based on NMF and random perturbation technology, which implements the recommendation system and solves the protection problem of user privacy data in the recommendation process on cloud computing. Compared with the privacy protection algorithm of SVD, the elements of the matrix after the decomposition of the new algorithm are non-negative elements, avoiding the meaninglessness of negative numbers in the matrix formed by texts, images, etc., and it has a good explanation for the local characteristics of things. Experiments show that the new algorithm can produce recommendation results with certain accuracy under the premise of protecting users' personal privacy on cloud computing.
He, Dongjie, Li, Haofeng, Wang, Lei, Meng, Haining, Zheng, Hengjie, Liu, Jie, Hu, Shuangwei, Li, Lian, Xue, Jingling.  2019.  Performance-Boosting Sparsification of the IFDS Algorithm with Applications to Taint Analysis. 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE). :267–279.
The IFDS algorithm can be compute-and memoryintensive for some large programs, often running for a long time (more than expected) or terminating prematurely after some time and/or memory budgets have been exhausted. In the latter case, the corresponding IFDS data-flow analyses may suffer from false negatives and/or false positives. To improve this, we introduce a sparse alternative to the traditional IFDS algorithm. Instead of propagating the data-flow facts across all the program points along the program’s (interprocedural) control flow graph, we propagate every data-flow fact directly to its next possible use points along its own sparse control flow graph constructed on the fly, thus reducing significantly both the time and memory requirements incurred by the traditional IFDS algorithm. In our evaluation, we compare FLOWDROID, a taint analysis performed by using the traditional IFDS algorithm, with our sparse incarnation, SPARSEDROID, on a set of 40 Android apps selected. For the time budget (5 hours) and memory budget (220GB) allocated per app, SPARSEDROID can run every app to completion but FLOWDROID terminates prematurely for 9 apps, resulting in an average speedup of 22.0x. This implies that when used as a market-level vetting tool, SPARSEDROID can finish analyzing these 40 apps in 2.13 hours (by issuing 228 leak warnings) while FLOWDROID manages to analyze only 30 apps in the same time period (by issuing only 147 leak warnings).
Llewellynn, Tim, Fernández-Carrobles, M. Milagro, Deniz, Oscar, Fricker, Samuel, Storkey, Amos, Pazos, Nuria, Velikic, Gordana, Leufgen, Kirsten, Dahyot, Rozenn, Koller, Sebastian et al..  2017.  BONSEYES: Platform for Open Development of Systems of Artificial Intelligence: Invited Paper. Proceedings of the Computing Frontiers Conference. :299–304.

The Bonseyes EU H2020 collaborative project aims to develop a platform consisting of a Data Marketplace, a Deep Learning Toolbox, and Developer Reference Platforms for organizations wanting to adopt Artificial Intelligence. The project will be focused on using artificial intelligence in low power Internet of Things (IoT) devices ("edge computing"), embedded computing systems, and data center servers ("cloud computing"). It will bring about orders of magnitude improvements in efficiency, performance, reliability, security, and productivity in the design and programming of systems of artificial intelligence that incorporate Smart Cyber-Physical Systems (CPS). In addition, it will solve a causality problem for organizations who lack access to Data and Models. Its open software architecture will facilitate adoption of the whole concept on a wider scale. To evaluate the effectiveness, technical feasibility, and to quantify the real-world improvements in efficiency, security, performance, effort and cost of adding AI to products and services using the Bonseyes platform, four complementary demonstrators will be built. Bonseyes platform capabilities are aimed at being aligned with the European FI-PPP activities and take advantage of its flagship project FIWARE. This paper provides a description of the project motivation, goals and preliminary work.

Le, Thao, Di, Jia, Tehranipoor, Mark, Forte, Domenic, Wang, Lei.  2016.  Tracking Data Flow at Gate-Level Through Structural Checking. Proceedings of the 26th Edition on Great Lakes Symposium on VLSI. :185–189.

The rapid growth of Internet-of-things and other electronic devices make a huge impact on how and where data travel. The confidential data (e.g., personal data, financial information) that travel through unreliable channels can be exposed to attackers. In hardware, the confidential data such as secret cipher keys are facing the same issue. This problem is even more serious when the IP is from a 3rd party and contains scan-chains. Thus, data flow tracking is important to analyze possible leakage channels in fighting against such hardware security threats. This paper introduces a method for tracking data flow and detecting potential hardware Trojans in gate-level soft IPs using assets and Structural Checking tool.