Visible to the public Biblio

Filters: Author is Zhang, Jie  [Clear All Filters]
Cao, Yuan, Zhao, Yongli, Li, Jun, Lin, Rui, Zhang, Jie, Chen, Jiajia.  2019.  Reinforcement Learning Based Multi-Tenant Secret-Key Assignment for Quantum Key Distribution Networks. 2019 Optical Fiber Communications Conference and Exhibition (OFC). :1–3.
We propose a reinforcement learning based online multi-tenant secret-key assignment algorithm for quantum key distribution networks, capable of reducing tenant-request blocking probability more than half compared to the benchmark heuristics.
Zhang, Kai, Liu, Chuanren, Zhang, Jie, Xiong, Hui, Xing, Eric, Ye, Jieping.  2017.  Randomization or Condensation?: Linear-Cost Matrix Sketching Via Cascaded Compression Sampling Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. :615–623.
Matrix sketching is aimed at finding compact representations of a matrix while simultaneously preserving most of its properties, which is a fundamental building block in modern scientific computing. Randomized algorithms represent state-of-the-art and have attracted huge interest from the fields of machine learning, data mining, and theoretic computer science. However, it still requires the use of the entire input matrix in producing desired factorizations, which can be a major computational and memory bottleneck in truly large problems. In this paper, we uncover an interesting theoretic connection between matrix low-rank decomposition and lossy signal compression, based on which a cascaded compression sampling framework is devised to approximate an m-by-n matrix in only O(m+n) time and space. Indeed, the proposed method accesses only a small number of matrix rows and columns, which significantly improves the memory footprint. Meanwhile, by sequentially teaming two rounds of approximation procedures and upgrading the sampling strategy from a uniform probability to more sophisticated, encoding-orientated sampling, significant algorithmic boosting is achieved to uncover more granular structures in the data. Empirical results on a wide spectrum of real-world, large-scale matrices show that by taking only linear time and space, the accuracy of our method rivals those state-of-the-art randomized algorithms consuming a quadratic, O(mn), amount of resources.
Sun, Degang, Zhang, Jie, Fan, Wei, Wang, Tingting, Liu, Chao, Huang, Weiqing.  2016.  SPLM: Security Protection of Live Virtual Machine Migration in Cloud Computing. Proceedings of the 4th ACM International Workshop on Security in Cloud Computing. :2–9.

Virtual machine live migration technology, as an important support for cloud computing, has become a central issue in recent years. The virtual machines' runtime environment is migrated from the original physical server to another physical server, maintaining the virtual machines running at the same time. Therefore, it can make load balancing among servers and ensure the quality of service. However, virtual machine migration security issue cannot be ignored due to the immature development of it. This paper we analyze the security threats of the virtual machine migration, and compare the current proposed protection measures. While, these methods either rely on hardware, or lack adequate security and expansibility. In the end, we propose a security model of live virtual machine migration based on security policy transfer and encryption, named as SPLM (Security Protection of Live Migration) and analyze its security and reliability, which proves that SPLM is better than others. This paper can be useful for the researchers to work on this field. The security study of live virtual machine migration in this paper provides a certain reference for the research of virtualization security, and is of great significance.