Publications by Reza Shokri | EPFL Graph Search

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Recent developments in information and communication technologies have been profound and life-changing. Most people are now equipped with smart phones with high computation power and communication capabilities. These devices can efficiently run multiple so ...

Recent smartphones incorporate embedded GPS devices that enable users to obtain geographic information about their surroundings by providing a location-based service (LBS) with their current coordinates. However, LBS providers collect a significant amount ...

The mainstream approach to protecting the location-privacy of mobile users in location-based services (LBSs) is to alter the users' actual locations in order to reduce the location information exposed to the service provider. The location obfuscation algor ...

Mobile users increasingly report their co-locations with other users, in addition to revealing their locations to online services. For instance, they tag the names of the friends they are with, in the messages and in the pictures they post on social networ ...

Co-location information about users is increasingly available online. For instance, mobile users more and more frequently report their co-locations with other users in the messages and in the pictures they post on social networking websites by tagging the ...

Location-aware smartphones support various location-based services (LBSs): users query the LBS server and learn on the fly about their surroundings. However, such queries give away private information, enabling the LBS to track users. We address this probl ...

It is a well-known fact that the progress of personal communication devices leads to serious concerns about privacy in general, and location privacy in particular. As a response to these issues, a number of Location-Privacy Protection Mechanisms (LPPMs) ha ...

Location check-ins contain both geographical and semantic information about the visited venues, in the form of tags (e.g., “restaurant”). Such data might reveal some personal information about users beyond what they actually want to disclose, hence their p ...

In recommender systems, usually, a central server needs to have access to users' profiles in order to generate useful recommendations. Having this access, however, undermines the users' privacy. The more information is revealed to the server on the user-it ...

Mobile users expose their location to potentially untrusted entities by using location-based services. Based on the frequency of location exposure in these applications, we divide them into two main types: Continuous and Sporadic. These two location exposu ...

In mobile wireless networks, third parties can track the location of mobile nodes by monitoring the pseudonyms used for identification. A frequently proposed solution to protect the location privacy of mobile nodes suggests to change pseudonyms in regions ...

Location-aware smart phones support various location-based services (LBSs): users query the LBS server and learn on the fly about their surroundings. However, such queries give away private information, enabling the LBS to identify and track users. We addr ...

We analytically investigate how over-parameterization of models in randomized machine learning algorithms impacts the information leakage about their training data. Specifically, we prove a privacy bound for the KL divergence between model distributions on ...

In modern mobile networks, users increasingly share their location with third-parties in return for location-based services. In this way, users obtain services customized to their location. Yet, such communications leak location information about users. Ev ...

Wireless networking relies on a fundamental building block, neighbor discovery (ND). The nature of wireless communications, however, makes attacks against ND easy: An adversary can simply replay or relay (wormhole) packets across the network and mislead di ...

There is a rich collection of literature that aims at protecting the privacy of users querying location-based services. One of the most popular location privacy techniques consists in cloaking users' locations such that k users appear as potential senders ...

As privacy moves to the center of attention in networked systems, and the need for trust remains a necessity, an important question arises: How do we reconcile the two seemingly contradicting requirements? In this paper, we show that the notion of data-cen ...

Reza Shokri

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Quantifying and Protecting Location Privacy

Collaborative Location Privacy with Rational Users

Protecting Location Privacy: Optimal Strategy against Localization Attacks

Quantifying the Effect of Co-location Information on Location Privacy

Quantifying Interdependent Privacy Risks with Location Data

Hiding in the Mobile Crowd: Location Privacy through Collaboration

Quantifying Location Privacy

Predicting Users' Motivations behind Location Check-Ins and Utility Implications of Privacy Protection Mechanisms

Preserving Privacy in Collaborative Filtering through Distributed Aggregation of Offline Profiles

Quantifying Location Privacy: The Case of Sporadic Location Exposure

On the Optimal Placement of Mix Zones

Collaborative Location Privacy

Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks

Evaluating the Privacy Risk of Location-Based Services

A Practical Secure Neighbor Verification Protocol for Wireless Sensor Networks

Unraveling an Old Cloak: k-anonymity for Location Privacy

On the Tradeoff between Trust and Privacy in Wireless Ad Hoc Networks