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 …

Abstract Web Data Extraction is an important problem that has been studied by means of
different scientific tools and in a broad range of applications. Many approaches to extracting …

In a membership inference attack, an attacker aims to infer whether a data sample is in a
target classifier's training dataset or not. Specifically, given a black-box access to the target …

Recommender systems (RS), serving at the forefront of Human-centered AI, are widely
deployed in almost every corner of the web and facilitate the human decision-making …

While trawling online/offline password guessing has been intensively studied, only a few
studies have examined targeted online guessing, where an attacker guesses a specific …

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 …

Differential privacy (DP) is a widely accepted mathematical framework for protecting data
privacy. Simply stated, it guarantees that the distribution of query results changes only …

Differential privacy provides strong privacy preservation guarantee in information sharing.
As social network analysis has been enjoying many applications, it opens a new arena for …

Along with the popularity of mobile social networks (MSNs) is the increasing danger of
privacy breaches due to user location exposures. In this work, we take an initial step towards …

We study how potential attackers can identify accounts on different social network sites that
all belong to the same user, exploiting only innocuous activity that inherently comes with …