Related papers: FIRST: FrontrunnIng Resilient Smart ConTracts
First come, first served: Critical choices between alternative actions are often made based on events external to an organization, and reacting promptly to their occurrence can be a major advantage over the competition. In Business Process…
The trend towards delegating data processing to a remote party raises major concerns related to privacy violations for both end-users and service providers. These concerns have attracted the attention of the research community, and several…
Vertical federated learning (VFL) is attracting much attention because it enables cross-silo data cooperation in a privacy-preserving manner. While most research works in VFL focus on linear and tree models, deep models (e.g., neural…
Federated learning is known to be vulnerable to both security and privacy issues. Existing research has focused either on preventing poisoning attacks from users or on concealing the local model updates from the server, but not both.…
Federated learning enables different parties to collaboratively build a global model under the orchestration of a server while keeping the training data on clients' devices. However, performance is affected when clients have heterogeneous…
Modern user-facing latency-sensitive web services include numerous distributed, intercommunicating microservices that promise to simplify software development and operation. However, multiplexing of compute resources across microservices is…
This work presents vQFL (vehicular Quantum Federated Learning), a new framework that leverages quantum machine learning techniques to tackle key privacy and security issues in autonomous vehicular networks. Furthermore, we propose a…
Fraudulent transactions and how to detect them remain a significant problem for financial institutions around the world. The need for advanced fraud detection systems to safeguard assets and maintain customer trust is paramount for…
Federated Learning (FL) enables clients to collaboratively train a global model using their local datasets while reinforcing data privacy, but it is prone to poisoning attacks. Existing defense mechanisms assume that clients' data are…
This paper presents the PUF finite state machine (PUF-FSM) that is served as a practical {\it controlled} strong PUF. Previous controlled PUF designs have the difficulties of stabilizing the noisy PUF responses where the error correction…
Most currently used cryptographic tools for protecting data are based on certain computational assumptions, which makes them vulnerable with respect to technological and algorithmic developments, such as quantum computing. One existing…
Physically Unclonable Functions (PUFs) are potential security blocks to generate unique and more secure keys in low-cost cryptographic applications. Dynamic random-access memory (DRAM) has been proposed as one of the promising candidates…
Smart contracts are programs that execute transactions involving independent parties and cryptocurrencies. As programs, smart contracts are susceptible to a wide range of errors and vulnerabilities. Such vulnerabilities can result in…
Federated Learning (FL) is a collaborative machine learning approach allowing participants to jointly train a model without having to share their private, potentially sensitive local datasets with others. Despite its benefits, FL is…
This paper introduces a novel, fully distributed control framework for DC microgrids, enhancing resilience against exponentially unbounded false data injection (EU-FDI) attacks. Our framework features a consensus-based secondary control for…
In this paper, we prove classical coin-flipping secure in the presence of quantum adversaries. The proof uses a recent result of Watrous [Wat09] that allows quantum rewinding for protocols of a certain form. We then discuss two…
In this paper we provide a high performance solution to the problem of committing transactions while enforcing a predefined order. We provide the design and implementation of three algorithms, which deploy a specialized cooperative…
Electing democratic representatives via voting has been a common mechanism since the 17th century. However, these mechanisms raise concerns about fairness, privacy, vote concealment, fair calculations of tally, and proxies voting on their…
Physical unclonable functions(PUFs) provide a unique fingerprint to a physical entity by exploiting the inherent physical randomness. Gao et al. discussed the vulnerability of most current-day PUFs to sophisticated machine learning-based…
Rapid growth of digital transactions has led to a surge in fraudulent activities, challenging traditional detection methods in the financial sector. To tackle this problem, we introduce a specialised federated learning framework that…