Publication

Privacy in Recommender Systems

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The area of privacy preserving machine learning has been of growing importance in practice, which has lead to an increased interest in this topic in both academia and industry. We have witnessed this through numerous papers and systems published and develo ...

ACM2019

The ever-growing amount of online information calls for Personalization. Among the various personalization systems, recommenders have become increasingly popular in recent years. Recommenders typically use collaborative filtering to suggest the most releva ...

EPFL2018

A Predictive Model for User Motivation and Utility Implications of Privacy-Protection Mechanisms in Location Check-Ins

Jean-Pierre Hubaux, Kévin Clément Huguenin, Igor Bilogrevic, Reza Shokri, Italo Ivan Dacosta Petrocelli, Joana Catarina Soares Machado, Stefan Mihaila

Location check-ins contain both geographical and semantic information about the visited venues. Semantic information is usually represented by means of tags (e.g., “restaurant”). Such data can reveal some personal information about users beyond what they a ...

2018

Topics in Educational Cyber-Physical Labs

Wissam Halimi

Recent advances in remote sensing and actuation technologies, coupled with the large reach of the internet, allowed for the emergence of applications such as cyber-physical labs. Cyber-physical labs are the digital and remotely-accessible equivalent of the ...

EPFL2018

On the linear convergence of the stochastic gradient method with constant step-size

Volkan Cevher, Cong Bang Vu

The strong growth condition (SGC) is known to be a sufficient condition for linear convergence of the stochastic gradient method using a constant step-size γ (SGM-CS). In this paper, we provide a necessary condition, for the linear convergence of SGM-CS, t ...

2018

Scrub is a troubleshooting tool for distributed applications that operate under strict SLOs common in production environments. It allows users to formulate queries on events occurring during execution in order to assess the correctness of the application’s ...

2018

Towards Usable Checksums: Automating the Integrity Verification ofWeb Downloads for the Masses

Mathias Jacques Jean-Marc Humbert, Kévin Clément Huguenin, Igor Bilogrevic, Mauro Cherubini, Bertil Chapuis, Alexandre Meylan

Internet users can download software for their computers from app stores (e.g., Mac App Store and Windows Store) or from other sources, such as the developers' websites. Most Internet users in the US rely on the latter, according to our representative stud ...

ASSOC COMPUTING MACHINERY2018

Data-Driven, Personalized Usable Privacy

Hamza Harkous

We live in the "inverse-privacy" world, where service providers derive insights from users' data that the users do not even know about. This has been fueled by the advancements in machine learning technologies, which allowed providers to go beyond the supe ...

EPFL2017

Context-Dependent Privacy-Aware Photo Sharing based on Machine Learning

Touradj Ebrahimi, Lin Yuan, Joël Régis Theytaz

Photo privacy has raised a growing concern with the advancements of image analytics, face recognition, and deep learning techniques widely applied on social media. If properly deployed, these powerful techniques can in turn assist people in enhancing their ...

2017

"If You Can't Beat Them, Join Them'': A Usability Approach to Interdependent Privacy in Cloud Apps

Karl Aberer, Hamza Harkous

Cloud storage services, like Dropbox and Google Drive, have growing ecosystems of 3rd party apps that are designed to work with users’ cloud files. Such apps often request full access to users’ files, including files shared with collaborators. Hence, whene ...

2017