相关论文: Privacy Limitations Of Interest-based Advertising …
Google's recent update to the manifest file for Chrome browser extensions, transitioning from manifest version 2 (MV2) to manifest version 3 (MV3), has raised concerns among users and ad blocker providers, who worry that the new…
While advertising has become commonplace in today's online interactions, there is a notable dearth of research investigating the extent to which browser fingerprinting is harnessed for user tracking and targeted advertising. Prior studies…
The current business model for existing recommender services is centered around the availability of users' personal data at their side whereas consumers have to trust that the recommender service providers will not use their data in a…
This paper proposes a new recommendation system preserving both privacy and utility. It relies on the local differential privacy (LDP) for the browsing user to transmit his noisy preference profile, as perturbed Bloom filters, to the…
The widespread adoption of continuously connected smartphones and tablets developed the usage of mobile applications, among which many use location to provide geolocated services. These services provide new prospects for users: getting…
Major advertising platforms recently increased privacy protections by limiting advertisers' access to individual-level data. Instead of providing access to granular raw data, the platforms only allow a limited number of aggregate queries to…
Countries across the globe have been pushing strict regulations on the protection of personal or private data collected. The traditional centralized machine learning method, where data is collected from end-users or IoT devices, so that it…
With the emergence of data silos and popular privacy awareness, the traditional centralized approach of training artificial intelligence (AI) models is facing strong challenges. Federated learning (FL) has recently emerged as a promising…
When surfing the Internet, individuals leak personal and corporate information to third parties whose (legitimate or not) businesses revolve around the value of collected data. The implications are serious, from a person unwillingly…
Collaborative filtering (CF) based recommender systems are typically trained based on personal interaction data (e.g., clicks and purchases) that could be naturally represented as ego graphs. However, most existing recommendation methods…
Websites use third-party ads and tracking services to deliver targeted ads and collect information about users that visit them. These services put users' privacy at risk, and that is why users' demand for blocking these services is growing.…
Third-party tracking is common on almost all commercially operated websites. Prior work has studied in detail the extent of third-party tracking on the web, detection of third-party trackers, and defending against third-party tracking.…
A point-of-interest (POI) recommendation system performs an important role in location-based services because it can help people to explore new locations and promote advertisers to launch advertisements at appropriate locations. The…
This Article examines Google's dominance of the browser market, highlighting how Google's Chrome browser plays a critical role in reinforcing Google's dominance in other markets. While Google portrays Chrome as a neutral platform built on…
Federated learning (FL) has sparked extensive interest in exploiting the private data on clients' local devices. However, the parameter server setting of FL not only has high bandwidth requirements, but also poses data privacy issues and a…
Third-party web tracking is a common, and broadly used technique on the Web. Almost every step of users' is tracked, analyzed, and later used in different use cases (e.g., online advertisement). Different defense mechanisms have emerged to…
With the rapid development of artificial intelligence (AI), ethical issues surrounding AI have attracted increasing attention. In particular, autonomous vehicles may face moral dilemmas in accident scenarios, such as staying the course…
Motivated by tensions between data privacy for individual citizens, and societal priorities such as counterterrorism and the containment of infectious disease, we introduce a computational model that distinguishes between parties for whom…
The growing public concerns on data privacy in face recognition can be greatly addressed by the federated learning (FL) paradigm. However, conventional FL methods perform poorly due to the uniqueness of the task: broadcasting class centers…
A variety of tools have been introduced recently that are designed to help people protect their privacy on the Internet. These tools perform many different functions in-cluding encrypting and/or anonymizing communications, preventing the…