Publications associées à A Location-Privacy Threat Stemming from the Use of Shared Public IP Addresses

Publications associées (33)

Behavioral Homophily in Social Media via Inverse Reinforcement Learning: A Reddit Case Study

Online communities play a critical role in shaping societal discourse and influencing collective behavior in the real world. The tendency for people to connect with others who share similar characteristics and views, known as homophily, plays a key role in ...

ACM2025

Intermediate Address Space: virtual memory optimization of heterogeneous architectures for cache-resident workloads

Qunyou Liu, David Atienza Alonso, Marina Zapater Sancho, Darong Huang, Luis Maria Costero Valero

The increasing demand for computing power and the emergence of heterogeneous computing architectures have driven the exploration of innovative techniques to address current limitations in both the compute and memory subsystems. One such solution is the use ...

2024

The Privacy Power of Correlated Noise in Decentralized Learning

Rachid Guerraoui, Martin Jaggi, Youssef Allouah, Anastasiia Koloskova

Decentralized learning is appealing as it enables the scalable usage of large amounts of distributed data and resources (without resorting to any central entity), while promoting privacy since every user minimizes the direct exposure of their data. Yet, wi ...

PMLR2024

Influence of micro-patterned support properties and interfacial polymerization conditions on performance of patterned thin-film composite membranes

Kumar Varoon Agrawal, Cédric Karel J Van Goethem

Patterned membranes prepared via spray-modified non -solvent induced phase separation (s-NIPS) have successfully shown enhanced fluxes and better fouling control, thanks to the increased surface area and high fluid shear at the membrane/feed interface. The ...

Elsevier2024

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Anastasiia Koloskova

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

EPFL2024

Differentially private multi-agent constraint optimization

Boi Faltings, Sujit Prakash Gujar, Aleksei Triastcyn, Sankarshan Damle

Distributed constraint optimization (DCOP) is a framework in which multiple agents with private constraints (or preferences) cooperate to achieve a common goal optimally. DCOPs are applicable in several multi-agent coordination/allocation problems, such as ...

Springer2024

A Privacy-Preserving Querying Mechanism with High Utility for Electric Vehicles

Sayan Biswas

Electric vehicles (EVs) are becoming more popular due to environmental consciousness. The limited availability of charging stations (CSs), compared to the number of EVs on the road, has led to increased range anxiety and a higher frequency of CS queries du ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Touch and Closeness in Naturally Organized Activities

Alain Bovet

Countless aspects of touch and closeness have been questioned in an unprecedented way during the recent Covid epidemic. Social practices as banal as greetings were both reflexively and practically challenged and sometimes deeply altered, resulting in painf ...

2024

The Architectural Space and the Healing Bed: On the Dimension of Privacy in Hospital Architectures

Anja Fröhlich

Building Early Modern Privacy Organizer: Centre for Privacy Studies, University of Copenhagen, and the Royal Danish Academy—Architecture, Design, Conservation, Institute of Architecture and Design The PRIVACY research team examines how notions of privacy ...

2024

On the fundamental limits of privacy-enhancing technologies

Theresa Stadler

The advancement of data-driven decision making has become a key priority in political agendas and private sector strategies. However, the growing demand for data and the opacity of data-driven systems raise concerns about the threats to individual privacy ...

EPFL2024

Entrepreneurial Opportunities and Threats in the Era of Grand Societal Challenges

Enrico Bergamini

We live in an era defined by attempts to grapple with an ever-expanding array of grand societal challenges (GCs). These challenges comprise transformational social and environmental issues, such as environmental degradation and global pandemics, and the cr ...

EPFL2023

Rebooting Virtual Memory with Midgard

Siddharth Gupta

Virtual Memory (VM) is a critical programming abstraction that is widely used in various modern computing platforms. With the rise of datacenter computing and birth of planet-scale online services, the semantic and capacity requirements from memory have ev ...

EPFL2023

Challenging the Assumptions: Rethinking Privacy, Bias, and Security in Machine Learning

Bogdan Kulynych

Predictive models based on machine learning (ML) offer a compelling promise: bringing clarity and structure to complex natural and social environments. However, the use of ML poses substantial risks related to the privacy of their training data as well as ...

EPFL2023

Sustainability and Ethicality are Peripheral to Students’ Software Design

Bryan Alexander Ford, Siara Ruth Isaac, Aditi Kothiyal, Pierluca Borsò

The conceptual design phase is a fascinating moment to observe how a design task is interpreted, as the (often implicit) relative importance students accord to the various requirements and constraints offers a window into the thinking underpinning their de ...

Tempus Publications2023

P3LI5: Practical and confidEntial Lawful Interception on the 5G core

Francesco Intoci, Apostolos Pyrgelis

Lawful Interception (LI) is a legal obligation of Communication Service Providers (CSPs) to provide interception capabilities to Law Enforcement Agencies (LEAs) in order to gain insightful data from network communications for criminal proceedings, e.g., ne ...

IEEE2023

Arbitrary Decisions are a Hidden Cost of Differentially Private Training

Carmela González Troncoso, Bogdan Kulynych

Mechanisms used in privacy-preserving machine learning often aim to guarantee differential privacy (DP) during model training. Practical DP-ensuring training methods use randomization when fitting model parameters to privacy-sensitive data (e.g., adding Ga ...

Assoc Computing Machinery2023

Caching and Neutrality

Aikaterini Argyraki, Pavlos Nikolopoulos, Muhammad Abdullah

We are used to defining network neutrality as absence of traffic differentiation, like policing or shaping. These mechanisms, however, are often not what determines end-users’ quality of experience (QoE). Most content today is accessed through edge caches, ...

ACM Association for Computing Machinery2023

Privacy-Preserving Machine Learning on Graphs

Sina Sajadmanesh

Graph Neural Networks (GNNs) have emerged as a powerful tool for learning on graphs, demonstrating exceptional performance in various domains. However, as GNNs become increasingly popular, new challenges arise. One of the most pressing is the need to ensur ...

EPFL2023

Roadmap on Optical Sensors

Lei Zhang, Luc Thévenaz

Optical sensors and sensing technologies are playing a more and more important role in our modern world. From micro-probes to large devices used in such diverse areas like medical diagnosis, defense, monitoring of industrial and environmental conditions, o ...

2023

Adapting Haptic Feedback for Guided Meditation

Nicolas Henchoz, Cédric Duchene, Andreas Sonderegger, Emily Clare Groves, Romain Simon Collaud, Yoann Pierre Douillet

Technology supporting meditation is a multimillion-dollar market that continues to grow. There is also strong academic interest to understand and improve the impact technology can have for the user experience of practitioners. However, little work investig ...

2023