Publications de Hamza Harkous | EPFL Graph Search

Invited Paper: The Applications of Machine Learning in Privacy Notice and Choice

For more than two decades since the rise of the World Wide Web, the "Notice and Choice" framework has been the governing practice for the disclosure of online privacy practices. The emergence of new forms of user interactions, such as voice, and the enforc ...

IEEE2019

Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep Learning

Karl Aberer, Rémi Philippe Lebret, Hamza Harkous, Kassem Fawaz

Privacy policies are the primary channel through which companies inform users about their data collection and sharing practices. These policies are often long and difficult to comprehend. Short notices based on information extracted from privacy policies h ...

USENIX ASSOC2018

280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia Using Character-Level Classification

Karl Aberer, Rémi Philippe Lebret, Hamza Harkous, Amit Gupta

We propose a novel fully-automated approach towards inducing multilingual taxonomies fromWikipedia. Given an English taxonomy, our approach first leverages the interlanguage links of Wikipedia to automatically construct training datasets for the is-a relat ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2018

Taxonomy Induction Using Hypernym Subsequences

Karl Aberer, Amit Gupta, Rémi Philippe Lebret, Hamza Harkous

We propose a novel, semi-supervised approach towards domain taxonomy induction from an input vocabulary of seed terms. Unlike all previous approaches, which typically extract direct hypernym edges for terms, our approach utilizes a novel probabilistic fram ...

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

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

Data-Driven Privacy Indicators

Karl Aberer, Hamza Harkous, Rameez Rahman

Third party applications work on top of existing platforms that host users’ data. Although these apps access this data to provide users with specific services, they can also use it for monetization or profiling purposes. In practice, there is a significant ...

2016

The Curious Case of the PDF Converter that Likes Mozart: Dissecting and Mitigating the Privacy Risk of Personal Cloud Apps

Karl Aberer, Hamza Harkous, Rameez Rahman, Bojan Karlas

Third party apps that work on top of personal cloud services such as Google Drive and Dropbox, require access to the user’s data in order to provide some functionality. Through detailed analysis of a hundred popular Google Drive apps from Google’s Chrome s ...

2016

PriBots: Conversational Privacy with Chatbots

Karl Aberer, Hamza Harkous, Kassem Fawaz

Traditional mechanisms for delivering notice and enabling choice have so far failed to protect users’ privacy. Users are continuously frustrated by complex privacy policies, unreachable privacy settings, and a multitude of emerging standards. The miniaturi ...

2016

Dissecting UbuntuOne: Autopsy of a Global-scale Personal Cloud Back-end

Marko Vukolic, Hamza Harkous, Yongchao Tian

Personal Cloud services, such as Dropbox or Box, have been widely adopted by users. Unfortunately, very little is known about the internal operation and general characteristics of Personal Clouds since they are proprietary services. In this paper, we fo ...

ACM Press2015