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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 ...

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 ...

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 ...

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 ...

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 ...

The advancement of face recognition technology has delivered substantial societal advantages. However, it has also raised global privacy concerns due to the ubiquitous collection and potential misuse of individuals' facial data. This presents a notable par ...

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 ...

Decentralized storage networks offer services with intriguing possibilities to reduce inequalities in an extremely centralized market. Fair distribution of rewards, however, is still a persistent problem in the current generation of decentralized applicati ...

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 ...

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 ...

With the pervasive digitalization of modern life, we benefit from efficient access to information and services. Yet, this digitalization poses severe privacy challenges, especially for special-needs individuals. Beyond being a fundamental human right, priv ...

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 ...

Humanitarian aid-distribution programs help bring physical goods to people in need. Traditional paper-based solutions to support aid distribution do not scale to large populations and are hard to secure. Existing digital solutions solve these issues, at th ...

One major challenge in distributed learning is to efficiently learn for each client when the data across clients is heterogeneous or non iid (not independent or identically distributed). This provides a significant challenge as the data of the other client ...

In this work, we carry out the first, in-depth, privacy analysis of Decentralized Learning-a collaborative machine learning framework aimed at addressing the main limitations of federated learning. We introduce a suite of novel attacks for both passive and ...

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 ...

We study the privatization of distributed learning and optimization strategies. We focus on differential privacy schemes and study their effect on performance. We show that the popular additive random perturbation scheme degrades performance because it is ...

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 ...

Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present ...

Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing, transferring, and centralizing the data from silos, however, is difficult due to current privacy regulations (e.g., H ...

Privacy-aware and highly-available OSN profiles

Chat with Graph Search

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

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

The Privacy Power of Correlated Noise in Decentralized Learning

Differentially private multi-agent constraint optimization

Touch and Closeness in Naturally Organized Activities

PRO-Face C: Privacy-Preserving Recognition of Obfuscated Face via Feature Compensation

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

Altruism, reciprocity, and tokens to reward forwarding data: Is that fair?

Privacy-Preserving Machine Learning on Graphs

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

Bridging the gap between theoretical and practical privacy technologies for at-risk populations

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

Not Yet Another Digital ID: Privacy-Preserving Humanitarian Aid Distribution

Personalized Privacy-Preserving Distributed Learning on Heterogeneous Data

On the (In)security of Peer-to-Peer Decentralized Machine Learning

Rebooting Virtual Memory with Midgard

Enforcing Privacy in Distributed Learning With Performance Guarantees

Entrepreneurial Opportunities and Threats in the Era of Grand Societal Challenges

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

Privacy-preserving federated neural network training and inference