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Zhu, Yan, Zhang, Yi, Wang, Jing, Song, Weijing, Chu, Cheng-Chung, Liu, Guowei.  2019.  From Data-Driven to Intelligent-Driven: Technology Evolution of Network Security in Big Data Era. 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). 2:103–109.

With the advent of the big data era, information systems have exhibited some new features, including boundary obfuscation, system virtualization, unstructured and diversification of data types, and low coupling among function and data. These features not only lead to a big difference between big data technology (DT) and information technology (IT), but also promote the upgrading and evolution of network security technology. In response to these changes, in this paper we compare the characteristics between IT era and DT era, and then propose four DT security principles: privacy, integrity, traceability, and controllability, as well as active and dynamic defense strategy based on "propagation prediction, audit prediction, dynamic management and control". We further discuss the security challenges faced by DT and the corresponding assurance strategies. On this basis, the big data security technologies can be divided into four levels: elimination, continuation, improvement, and innovation. These technologies are analyzed, combed and explained according to six categories: access control, identification and authentication, data encryption, data privacy, intrusion prevention, security audit and disaster recovery. The results will support the evolution of security technologies in the DT era, the construction of big data platforms, the designation of security assurance strategies, and security technology choices suitable for big data.

Zhu, Yan, Yang, Shuai, Chu, William Cheng-Chung, Feng, Rongquan.  2019.  FlashGhost: Data Sanitization with Privacy Protection Based on Frequent Colliding Hash Table. 2019 IEEE International Conference on Services Computing (SCC). :90–99.

Today's extensive use of Internet creates huge volumes of data by users in both client and server sides. Normally users don't want to store all the data in local as well as keep archive in the server. For some unwanted data, such as trash, cache and private data, needs to be deleted periodically. Explicit deletion could be applied to the local data, while it is a troublesome job. But there is no transparency to users on the personal data stored in the server. Since we have no knowledge of whether they're cached, copied and archived by the third parties, or sold by the service provider. Our research seeks to provide an automatic data sanitization system to make data could be self-destructing. Specifically, we give data a life cycle, which would be erased automatically when at the end of its life, and the destroyed data cannot be recovered by any effort. In this paper, we present FlashGhost, which is a system that meets this challenge through a novel integration of cryptography techniques with the frequent colliding hash table. In this system, data will be unreadable and rendered unrecoverable by overwriting multiple times after its validity period has expired. Besides, the system reliability is enhanced by threshold cryptography. We also present a mathematical model and verify it by a number of experiments, which demonstrate theoretically and experimentally our system is practical to use and meet the data auto-sanitization goal described above.