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We propose a new dynamic average consensus algorithm that is robust to information-sharing noise arising from differential-privacy design. Not only is dynamic average consensus widely used in cooperative control and distributed tracking, it…
Echomix is a practical mix network framework and a suite of associated protocols providing strong metadata privacy against realistic modern adversaries. It is distinguished from other anonymity systems by a resistance to traffic analysis by…
The proliferation of spam on the Web has necessitated the development of machine learning models to automate their detection. However, the dynamic nature of spam and the sophisticated evasion techniques employed by spammers often lead to…
Content blocking is an important part of a performant, user-serving, privacy respecting web. Most content blockers build trust labels over URLs. While useful, this approach has well understood shortcomings. Attackers may avoid detection by…
An oblivious pseudorandom function (OPRF) is a protocol by which a client and server interact to evaluate a pseudorandom function on a key provided by the server and an input provided by the client, without divulging the key or input to the…
The growing complexity of Internet of Things (IoT) environments, particularly in cross-domain data sharing, presents significant security challenges. Existing data-sharing schemes often rely on computationally expensive cryptographic…
Distributed anonymity services, such as onion routing networks or cryptocurrency tumblers, promise privacy protection without trusted third parties. While the security of these services is often well-researched, security implications of…
Consensus is fundamental for distributed systems since it underpins key functionalities of such systems ranging from distributed information fusion, decision-making, to decentralized control. In order to reach an agreement, existing…
Over the last decade, proliferation of various online platforms and their increasing adoption by billions of users have heightened the privacy risk of a user enormously. In fact, security researchers have shown that sparse microdata…
With the rapid evolution of Web3.0, cryptocurrency has become a cornerstone of decentralized finance. While these digital assets enable efficient and borderless financial transactions, their pseudonymous nature has also attracted malicious…
Federated learning enables the development of a machine learning model among collaborating agents without requiring them to share their underlying data. However, malicious agents who train on random data, or worse, on datasets with the…
Smart cities, which can monitor the real world and provide smart services in a variety of fields, have improved people's living standards as urbanization has accelerated. However, there are security and privacy concerns because smart city…
One of the defining features of Bitcoin and the thousands of cryptocurrencies that have been derived from it is a globally visible transaction ledger. While Bitcoin uses pseudonyms as a way to hide the identity of its participants, a long…
According to experts, one third of all IT vulnerabilities today are due to inadequate software verification. Internal program processes are not sufficiently secured against manipulation by attackers, especially if access has been gained.…
Security and privacy in Direct Load Control (DLC) is a fundamental challenge in smart grids. In this paper, we propose a blockchain-based framework to increase security and privacy of DLC. We propose a method whereby participating nodes…
One of the most widely used techniques for data clustering is agglomerative clustering. Such algorithms have been long used across many different fields ranging from computational biology to social sciences to computer vision in part…
Employee data can be used to facilitate work, but their misusage may pose risks for individuals. Inverse transparency therefore aims to track all usages of personal data, allowing individuals to monitor them to ensure accountability for…
Privacy preservation is a big concern for various sectors. To protect individual user data, one emerging technology is differential privacy. However, it still has limitations for datasets with frequent queries, such as the fast accumulation…
Adversarial black-box attacks aim to craft adversarial perturbations by querying input-output pairs of machine learning models. They are widely used to evaluate the robustness of pre-trained models. However, black-box attacks often suffer…
The increasing capabilities of deep neural networks for re-identification, combined with the rise in public surveillance in recent years, pose a substantial threat to individual privacy. Event cameras were initially considered as a…