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Anonymization techniques for privacy preserving data publishing: A comprehensive survey
A Majeed, S Lee - IEEE access, 2020 - ieeexplore.ieee.org
Anonymization is a practical solution for preserving user's privacy in data publishing. Data
owners such as hospitals, banks, social network (SN) service providers, and insurance …
owners such as hospitals, banks, social network (SN) service providers, and insurance …
Privacy inference attack against users in online social networks: a literature review
With the rapid development of social networks, users pay more and more attention to the
protection of personal information. However, the transmission of users' personal information …
protection of personal information. However, the transmission of users' personal information …
Safety and performance, why not both? bi-objective optimized model compression against heterogeneous attacks toward ai software deployment
The size of deep learning models in artificial intelligence (AI) software is increasing rapidly,
hindering the large-scale deployment on resource-restricted devices (eg., smartphones). To …
hindering the large-scale deployment on resource-restricted devices (eg., smartphones). To …
Safety and performance, why not both? bi-objective optimized model compression toward ai software deployment
The size of deep learning models in artificial intelligence (AI) software is increasing rapidly,
which hinders the large-scale deployment on resource-restricted devices (eg, smartphones) …
which hinders the large-scale deployment on resource-restricted devices (eg, smartphones) …
Security analysis on social media networks via STRIDE model
Security associated threats are often increased for online social media during a pandemic,
such as COVID-19, along with changes in a work environment. For example, employees in …
such as COVID-19, along with changes in a work environment. For example, employees in …
Privacy-preserving network embedding against private link inference attacks
Network embedding represents network nodes by a low-dimensional informative vector.
While it is generally effective for various downstream tasks, it may leak some private …
While it is generally effective for various downstream tasks, it may leak some private …
Privacy scoring over OSNs: Shared data granularity as a latent dimension
Privacy scoring aims at measuring the privacy violation risk of a user over an online social
network (OSN) based on attribute values shared in the user's OSN profile page and the …
network (OSN) based on attribute values shared in the user's OSN profile page and the …
Privacy-preserving recommendation with debiased obfuscaiton
C Lin, B Liu, X Zhang, Z Wang, C Hu… - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
As people enjoy the personalized services recommended by Recommender Systems (RSs),
the privacy disclosure risk increases with frequent interactions. Malicious adversary often …
the privacy disclosure risk increases with frequent interactions. Malicious adversary often …
Application of random forest in choosing a method of recovering the age of social network users
AA Korepanova, MV Abramov - Scientific and Technical Information …, 2022 - Springer
This article is devoted to the problem of recovering the ages of social network users by using
machine learning to combine the methods suggested in this article. Methods based on …
machine learning to combine the methods suggested in this article. Methods based on …
Hyobscure: Hybrid obscuring for privacy-preserving data publishing
Minimizing privacy leakage while ensuring data utility is a critical problem in a privacy-
preserving data publishing task, from which data holders can boost platform engagements …
preserving data publishing task, from which data holders can boost platform engagements …