Visible to the public Biblio

Filters: Author is Tian, H.  [Clear All Filters]
2019-05-09
Li, Y., Liu, X., Tian, H., Luo, C..  2018.  Research of Industrial Control System Device Firmware Vulnerability Mining Technology Based on Taint Analysis. 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS). :607-610.
Aiming at the problem that there is little research on firmware vulnerability mining and the traditional method of vulnerability mining based on fuzzing test is inefficient, this paper proposed a new method of mining vulnerabilities in industrial control system firmware. Based on taint analysis technology, this method can construct test cases specifically for the variables that may trigger vulnerabilities, thus reducing the number of invalid test cases and improving the test efficiency. Experiment result shows that this method can reduce about 23 % of test cases and can effectively improve test efficiency.
2018-04-02
Wei, R., Shen, H., Tian, H..  2017.  An Improved (k,p,l)-Anonymity Method for Privacy Preserving Collaborative Filtering. GLOBECOM 2017 - 2017 IEEE Global Communications Conference. :1–6.

Collaborative Filtering (CF) is a successful technique that has been implemented in recommender systems and Privacy Preserving Collaborative Filtering (PPCF) aroused increasing concerns of the society. Current solutions mainly focus on cryptographic methods, obfuscation methods, perturbation methods and differential privacy methods. But these methods have some shortcomings, such as unnecessary computational cost, lower data quality and hard to calibrate the magnitude of noise. This paper proposes a (k, p, I)-anonymity method that improves the existing k-anonymity method in PPCF. The method works as follows: First, it applies Latent Factor Model (LFM) to reduce matrix sparsity. Then it improves Maximum Distance to Average Vector (MDAV) microaggregation algorithm based on importance partitioning to increase homogeneity among records in each group which can retain better data quality and (p, I)-diversity model where p is attacker's prior knowledge about users' ratings and I is the diversity among users in each group to improve the level of privacy preserving. Theoretical and experimental analyses show that our approach ensures a higher level of privacy preserving based on lower information loss.