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20112023
most citedThe Power of Both Choices: Practical Load Balancing for Distributed Stream Processing Engines

124 citations · 1.2k across the 61 of their papers we have counts for

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Showing cs.CRShow all

11 papers · 1 filter

cs.CR2022★ 1 cited

A Survey on Privacy of Personal and Non-Personal Data in B5G/6G Networks

Chamara Sandeepa, Bartlomiej Siniarski, Nicolas Kourtellis +2

The upcoming Beyond 5G (B5G) and 6G networks are expected to provide enhanced capabilities such as ultra-high data rates, dense connectivity, and high scalability. It opens many po…

cs.CR2022★ 18 cited

The Hitchhiker's Guide to Facebook Web Tracking with Invisible Pixels and Click IDs

Paschalis Bekos, Panagiotis Papadopoulos, Evangelos P. Markatos +1

Over the past years, advertisement companies have used various tracking methods to persistently track users across the web. Such tracking methods usually include first and third-pa…

cs.CR2022

FLaaS: Cross-App On-device Federated Learning in Mobile Environments

Kleomenis Katevas, Diego Perino, Nicolas Kourtellis

Federated Learning (FL) has recently emerged as a popular solution to distributedly train a model on user devices improving user privacy and system scalability. Major Internet comp…

cs.CR2022★ 39 cited

A Survey on Privacy for B5G/6G: New Privacy Challenges, and Research Directions

Chamara Sandeepa, Bartlomiej Siniarski, Nicolas Kourtellis +2

Massive developments in mobile wireless telecommunication networks have been made during the last few decades. At present, mobile users are getting familiar with the latest 5G netw…

cs.CR2021★ 40 cited

PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments

Fan Mo, Hamed Haddadi, Kleomenis Katevas +3

We propose and implement a Privacy-preserving Federated Learning () framework for mobile systems to limit privacy leakages in federated learning. Leveraging the widespread pr…

cs.CR2020★ 27 cited

Not one but many Tradeoffs: Privacy Vs. Utility in Differentially Private Machine Learning

Benjamin Zi Hao Zhao, Mohamed Ali Kaafar, Nicolas Kourtellis

Data holders are increasingly seeking to protect their user's privacy, whilst still maximizing their ability to produce machine models with high quality predictions. In this work,…

cs.CR2019★ 7 cited

Clash of the Trackers: Measuring the Evolution of the Online Tracking Ecosystem

Konstantinos Solomos, Panagiotis Ilia, Sotiris Ioannidis +1

Websites are constantly adapting the methods used, and intensity with which they track online visitors. However, the wide-range enforcement of GDPR since one year ago (May 2018) fo…

cs.CR2019★ 4 cited

YourAdvalue: Measuring Advertising Price Dynamics without Bankrupting User Privacy

Michalis Pachilakis, Panagiotis Papadopoulos, Nikolaos Laoutaris +2

The Real Time Bidding (RTB) protocol is by now more than a decade old. During this time, a handful of measurement papers have looked at bidding strategies, personal information flo…

cs.CR2018★ 3 cited

Talon: An Automated Framework for Cross-Device Tracking Detection

Konstantinos Solomos, Panagiotis Ilia, Sotiris Ioannidis +1

Although digital advertising fuels much of today's free Web, it typically does so at the cost of online users' privacy, due to the continuous tracking and leakage of users' persona…

cs.CR2018★ 19 cited

LOBO -- Evaluation of Generalization Deficiencies in Twitter Bot Classifiers

Juan Echeverría, Emiliano De Cristofaro, Nicolas Kourtellis +3

Botnets in online social networks are increasingly often affecting the regular flow of discussion, attacking regular users and their posts, spamming them with irrelevant or offensi…

cs.CR2018

There goes Wally: Anonymously sharing your location gives you away

Apostolos Pyrgelis, Nicolas Kourtellis, Ilias Leontiadis +2

With current technology, a number of entities have access to user mobility traces at different levels of spatio-temporal granularity. At the same time, users frequently reveal thei…