TY - GEN
T1 - Cloud-Honeypot and Differential Privacy Empowered Interactive Heat Protection against Public Opinion Analysis
AU - Sun, Zhengyi
AU - Pan, Qianqian
AU - Li, Zhaohui
AU - Wu, Jun
N1 - Publisher Copyright:
©2023 IEEE.
PY - 2023
Y1 - 2023
N2 - With the rapid development of cloud social networks, the expression of opinions by individuals has witnessed significant growth. However, unregulated dissemination of public opinion can lead to severe consequences. This paper addresses the challenges associated with public opinion heat analysis, focusing on the potential misuse of user data by malicious entities. We propose a privacy protection mechanism that combines differential privacy theory and cloud-honeypot technologies, which is against scraper to ensure the legitimate use of data and safeguard user privacy. We propose a differential privacy mechanism with Laplace noise, which adds Laplace noise to the number of reposts, comments, and likes of the Weibo posts published by users, making it impossible for attackers to calculate the correct interactive heat and thus protecting the privacy of Weibo interaction popularity. At the same time, we propose a privacy protection mechanism which is applicable in combination with honeypot technology. After the cloud server identifies that the visitor is a malicious scraper, it redirects them to the honeypot webpage. This modified webpage incorporates additional Laplace noise, thereby enhancing the security measure. Additionally, we design an experiment to simulate the impact of adding noise to the data and compare the results to verify the feasibility of the differential privacy mechanism in countering scrapers. Finally, we discuss future directions for anti-scraping strategies based on differential privacy.
AB - With the rapid development of cloud social networks, the expression of opinions by individuals has witnessed significant growth. However, unregulated dissemination of public opinion can lead to severe consequences. This paper addresses the challenges associated with public opinion heat analysis, focusing on the potential misuse of user data by malicious entities. We propose a privacy protection mechanism that combines differential privacy theory and cloud-honeypot technologies, which is against scraper to ensure the legitimate use of data and safeguard user privacy. We propose a differential privacy mechanism with Laplace noise, which adds Laplace noise to the number of reposts, comments, and likes of the Weibo posts published by users, making it impossible for attackers to calculate the correct interactive heat and thus protecting the privacy of Weibo interaction popularity. At the same time, we propose a privacy protection mechanism which is applicable in combination with honeypot technology. After the cloud server identifies that the visitor is a malicious scraper, it redirects them to the honeypot webpage. This modified webpage incorporates additional Laplace noise, thereby enhancing the security measure. Additionally, we design an experiment to simulate the impact of adding noise to the data and compare the results to verify the feasibility of the differential privacy mechanism in countering scrapers. Finally, we discuss future directions for anti-scraping strategies based on differential privacy.
KW - cloud honeypot
KW - differential privacy
KW - interaction analysis
KW - interactive heat
UR - http://www.scopus.com/inward/record.url?scp=85183331788&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85183331788&partnerID=8YFLogxK
U2 - 10.1109/SmartCloud58862.2023.00040
DO - 10.1109/SmartCloud58862.2023.00040
M3 - Conference contribution
AN - SCOPUS:85183331788
T3 - Proceedings - 2023 IEEE 8th International Conference on Smart Cloud, SmartCloud 2023
SP - 188
EP - 193
BT - Proceedings - 2023 IEEE 8th International Conference on Smart Cloud, SmartCloud 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Smart Cloud, SmartCloud 2023
Y2 - 16 September 2023 through 18 September 2023
ER -