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- 저자 : Jiaxing Qu (검색결과 2 건)
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A Parallel Algorithm of String Matching Based on Message Passing Interface for Multicore Processors
Jiaxing Qu,Guoyin Zhang,Zhou Fang,Jiahui Liu 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.3
Multicore has long been considered an attractive platform for string matching. However, some existing traditional algorithms of string matching do not adapt to multicore platform, which pose new challenges to parallelism designs. In this paper, we introduce a multicore architecture with message passing interface to address these challenges. We exploit the popular Aho-Corasick algorithm for the string matching engine. Data parallelism is utilized to design optimization technique of string matching. The experiments show that an implementation of the 8-core system achieves up to 10.5 Gbps throughput on the average.
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Hybrid Recommendation Algorithm for User Satisfaction-oriented Privacy Model
Yinggang Sun,Hongguo Zhang,Luogang Zhang,Chao Ma,Hai Huang,Dongyang Zhan,Jiaxing Qu 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.10
Anonymization technology is an important technology for privacy protection in the process of data release. Usually, before publishing data, the data publisher needs to use anonymization technology to anonymize the original data, and then publish the anonymized data. However, for data publishers who do not have or have less anonymized technical knowledge background, how to configure appropriate parameters for data with different characteristics has become a more difficult problem. In response to this problem, this paper adds a historical configuration scheme resource pool on the basis of the traditional anonymization process, and configuration parameters can be automatically recommended through the historical configuration scheme resource pool. On this basis, a privacy model hybrid recommendation algorithm for user satisfaction is formed. The algorithm includes a forward recommendation process and a reverse recommendation process, which can respectively perform data anonymization processing for users with different anonymization technical knowledge backgrounds. The privacy model hybrid recommendation algorithm for user satisfaction described in this paper is suitable for a wider population, providing a simpler, more efficient and automated solution for data anonymization, reducing data processing time and improving the quality of anonymized data, which enhances data protection capabilities.
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