{AttriGuard}: A practical defense against attribute inference attacks via adversarial machine learning
Users in various web and mobile applications are vulnerable to attribute inference attacks, in
which an attacker leverages a machine learning classifier to infer a target user's private …
which an attacker leverages a machine learning classifier to infer a target user's private …
You are who you know and how you behave: Attribute inference attacks via users' social friends and behaviors
We propose new privacy attacks to infer attributes (eg, locations, occupations, and interests)
of online social network users. Our attacks leverage seemingly innocent user information …
of online social network users. Our attacks leverage seemingly innocent user information …
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 …
Privacy aspects of recommender systems
A Friedman, BP Knijnenburg, K Vanhecke… - Recommender systems …, 2015 - Springer
The popularity of online recommender systems has soared; they are deployed in numerous
websites and gather tremendous amounts of user data that are necessary for …
websites and gather tremendous amounts of user data that are necessary for …
Defending against machine learning based inference attacks via adversarial examples: Opportunities and challenges
As machine learning (ML) becomes more and more powerful and easily accessible,
attackers increasingly leverage ML to perform automated large-scale inference attacks in …
attackers increasingly leverage ML to perform automated large-scale inference attacks in …
[HTML][HTML] Towards user-oriented privacy for recommender system data: A personalization-based approach to gender obfuscation for user profiles
In this paper, we propose a new privacy solution for the data used to train a recommender
system, ie, the user–item matrix. The user–item matrix contains implicit information, which …
system, ie, the user–item matrix. The user–item matrix contains implicit information, which …
PrivStream: A privacy-preserving inference framework on IoT streaming data at the edge
Edge computing combining with artificial intelligence (AI) has enabled the timely processing
and analysis of streaming data produced by IoT intelligent applications. However, it causes …
and analysis of streaming data produced by IoT intelligent applications. However, it causes …
Attribute inference attacks in online multiplayer video games: A case study on Dota2
Did you know that over 70 million of Dota2 players have their in-game data freely
accessible? What if such data is used in malicious ways? This paper is the first to investigate …
accessible? What if such data is used in malicious ways? This paper is the first to investigate …
Incognito: A method for obfuscating web data
Users leave a trail of their personal data, interests, and intents while surfing or sharing
information on the Web. Web data could therefore reveal some private/sensitive information …
information on the Web. Web data could therefore reveal some private/sensitive information …
Preventing disclosure of personal data in IoT networks
Sharing data among applications is a growing phenomenon. With the IoT, this phenomenon
becomes more significant. As already studied in social networks, data sharing has the …
becomes more significant. As already studied in social networks, data sharing has the …