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Privacy is become major issue in distributed data mining. In the literature we can found many proposals of privacy preserving which can be divided into two major categories that is trusted third party and multiparty based privacy protocols.…
Cooperative learning, that enables two or more data owners to jointly train a model, has been widely adopted to solve the problem of insufficient training data in machine learning. Nowadays, there is an urgent need for institutions and…
The integration of Fog Computing with Flying Ad-Hoc Networks (FANETs) offers promising capabilities for decentralized, low-latency intelligence in UAV-based applications. However, the distributed nature, mobility, and resource constraints…
Motivated by cloud security concerns, there is an increasing interest in database systems that can store and support queries over encrypted data. A common architecture for such systems is to use a trusted component such as a cryptographic…
This paper presents a privacy-preserving protocol for identity registration and information sharing in federated authentication systems. The goal is to enable Identity Providers (IdPs) to detect duplicate or fraudulent identity enrollments…
We address differential privacy for fully distributed aggregative games with shared coupling constraints. By co-designing the generalized Nash equilibrium (GNE) seeking mechanism and the differential-privacy noise injection mechanism, we…
Platooning technologies enable trucks to drive cooperatively and automatically, providing benefits including less fuel consumption, greater road capacity, and safety. This paper introduces an aggregated zero-knowledge proof and…
Secure and privacy-preserving data aggregation in the Internet of Vehicles (IoV) continues to be a focal point of interest in both the industry and academia. Aiming at tackling the challenges and solving the remaining limitations of…
We study a protocol for distributed computation called shuffled check-in, which achieves strong privacy guarantees without requiring any further trust assumptions beyond a trusted shuffler. Unlike most existing work, shuffled check-in…
As eye tracking becomes pervasive with screen-based devices and head-mounted displays, privacy concerns regarding eye-tracking data have escalated. While state-of-the-art approaches for privacy-preserving eye tracking mostly involve…
For model privacy, local model parameters in federated learning shall be obfuscated before sent to the remote aggregator. This technique is referred to as \emph{secure aggregation}. However, secure aggregation makes model poisoning attacks…
Generative Adversarial Network (GAN) and its variants have recently attracted intensive research interests due to their elegant theoretical foundation and excellent empirical performance as generative models. These tools provide a promising…
For preserving privacy, blockchains can be equipped with dedicated mechanisms to anonymize participants. However, these mechanism often take only the abstraction layer of blockchains into account whereas observations of the underlying…
Secure aggregation is a cryptographic protocol that securely computes the aggregation of its inputs. It is pivotal in keeping model updates private in federated learning. Indeed, the use of secure aggregation prevents the server from…
Secure aggregation is a popular protocol in privacy-preserving federated learning, which allows model aggregation without revealing the individual models in the clear. On the other hand, conventional secure aggregation protocols incur a…
With the advent of the Internet of Things (IoT), e-health has become one of the main topics of research. Due to the sensitivity of patient information, patient privacy seems challenging. Nowadays, patient data is usually stored in the cloud…
Security and privacy are primary concerns in IoT management. Security breaches in IoT resources, such as smart sensors, can leak sensitive data and compromise the privacy of individuals. Effective IoT management requires a comprehensive…
Today, the publication of microdata poses a privacy threat. Vast research has striven to define the privacy condition that microdata should satisfy before it is released, and devise algorithms to anonymize the data so as to achieve this…
Blockchain technologies have overturned the digital finance industry by introducing a decentralized pseudonymous means of monetary transfer. The pseudonymous nature introduced privacy concerns, enabling various deanonymization techniques,…
Isolated qubits are a special class of quantum devices, which can be used to implement tamper-resistant cryptographic hardware such as one-time memories (OTM's). Unfortunately, these OTM constructions leak some information, and standard…