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Ȩ Ȩ > ¿¬±¸¹®Çå > ¿µ¹® ³í¹®Áö > TIIS (Çѱ¹ÀÎÅͳÝÁ¤º¸ÇÐȸ)

TIIS (Çѱ¹ÀÎÅͳÝÁ¤º¸ÇÐȸ)

Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) Hybrid Recommendation Algorithm for User Satisfaction-oriented Privacy Model
¿µ¹®Á¦¸ñ(English Title) Hybrid Recommendation Algorithm for User Satisfaction-oriented Privacy Model
ÀúÀÚ(Author) Yinggang Sun   Hongguo Zhang   Luogang Zhang   Chao Ma   Hai Huang   Dongyang Zhan   Jiaxing Qu  
¿ø¹®¼ö·Ïó(Citation) VOL 16 NO. 10 PP. 3419 ~ 3437 (2022. 10)
Çѱ۳»¿ë
(Korean Abstract)
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(English Abstract)
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.
Å°¿öµå(Keyword) Anonymization   Historical Configuration Scheme Resource Pool   Privacy Protection   Positive Recommendation Process   Reverse Recommendation Process   Satisfaction  
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