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Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees

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Protecting privacy through metadata analysis

Sandra Deepthy Siby

Although encryption hides the content of communications from third parties, metadata, i.e., the information attached to the content (such as the size or timing of communication) can be a rich source of details and context. In this dissertation, we demonstr ...

EPFL2022

On the advantages of P2P ML on mobile devices

Rachid Guerraoui, Alexandre David Olivier Maurer

Many fields make use nowadays of machine learning (ML) enhanced applications for cost optimization, scheduling or forecasting, in- cluding the energy sector. However, these very ML algorithms consume a significant amount of energy, sometimes going against ...

ACM2022

Data-Aware Privacy-Preserving Machine Learning

Aleksei Triastcyn

In this thesis, we focus on the problem of achieving practical privacy guarantees in machine learning (ML), where the classic differential privacy (DP) fails to maintain a good trade-off between user privacy and data utility. Differential privacy guarantee ...

EPFL2020

Bayesian Differential Privacy for Machine Learning

Boi Faltings, Aleksei Triastcyn

%0 Conference Paper %T Bayesian Differential Privacy for Machine Learning %A Aleksei Triastcyn %A Boi Faltings %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E A ...

2020

Private and Secure Distributed Learning

Georgios Damaskinos

The ever-growing number of edge devices (e.g., smartphones) and the exploding volume of sensitive data they produce, call for distributed machine learning techniques that are privacy-preserving. Given the increasing computing capabilities of modern edge de ...

EPFL2020

Generating Artificial Data for Private Deep Learning

Boi Faltings, Aleksei Triastcyn

In this paper, we propose generating artificial data that retain statistical properties of real data as the means of providing privacy for the original dataset. We use generative adversarial networks to draw privacy-preserving artificial data samples and d ...

2019

Inpainting in Omnidirectional Images for Privacy Protection

Touradj Ebrahimi, Evgeniy Upenik, Pinar Akyazi

Privacy protection is drawing more attention with the advances in image processing, visual and social media. Photo sharing is a popular activity, which also brings the concern of regulating permissions associated with shared content. This paper presents a ...

2019

A Truthful, Privacy-Preserving, Approximately Efficient Combinatorial Auction For Single-minded Bidders

Boi Faltings, Sujit Prakash Gujar, Sankarshan Damle

Combinatorial auctions are widely used to sell resources/items. The challenges in such auctions are multi-fold. We need to ensure that bidders, the strategic agents, bid their valuations truthfully to the auction mechanism. Besides, the agents may desire p ...

ASSOC COMPUTING MACHINERY2019

Privacy in Recommender Systems

Mahsa Taziki

The upsurge in the number of web users over the last two decades has resulted in a significant growth of online information. Recommenders are machine learning approach and are becoming one of the main ways to navigate the Internet. They recommend appropria ...

EPFL2018

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