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Security of model parameters and user data is critical for Transformer-based services, such as ChatGPT. While recent strides in secure two-party protocols have successfully addressed security concerns in serving Transformer models, their…
Secure routing protocols for mobile ad hoc networks have been developed recently, yet, it has been unclear what are the properties they achieve, as a formal analysis of these protocols is mostly lacking. In this paper, we are concerned with…
The hybrid hiding encryption algorithm, as its name implies, embraces concepts from both steganography and cryptography. In this exertion, an improved micro-architecture Field Programmable Gate Array (FPGA) implementation of this algorithm…
This white paper presents an analysis done by the MAMI project of the privacy and security concerns surrounding middlebox cooperation protocols (MCPs), based on our experimental experience with the Path Layer UDP Substrate (PLUS) proposal.…
This paper introduces the first two-dimensional XOR-based secret sharing scheme for layered multipath communication networks. We present a construction that guarantees successful message recovery and perfect privacy when an adversary…
GNSS are indispensable for various applications, but they are vulnerable to spoofing attacks. The original receiver autonomous integrity monitoring (RAIM) was not designed for securing GNSS. In this context, RAIM was extended with wireless…
Civilian-GNSS is vulnerable to signal spoofing attacks, and countermeasures based on cryptographic authentication are being proposed to protect against these attacks. Both Galileo and GPS are currently testing broadcast authentication…
For the modeling, design and planning of future energy transmission networks, it is vital for stakeholders to access faithful and useful power flow data, while provably maintaining the privacy of business confidentiality of service…
Gradient leakage attacks pose a significant threat to the privacy guarantees of federated learning. While distortion-based protection mechanisms are commonly employed to mitigate this issue, they often lead to notable performance…
Users may already have some perception of provided security based on experience with earlier generations. To maintain the stability and coherent integration of 5G services, it is imperative that security and privacy features prevalent in…
The purpose of this paper is to do a general survey on the existing communication modes inside a smart grid, the existing security loopholes and their countermeasures. Then we suggest a detailed countermeasure, building upon the Jigsaw…
Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link prediction. However, it is unfortunately accompanied by…
Biologically plausible Spiking Neural Networks (SNNs), characterized by spike sparsity, are growing tremendous attention over intellectual edge devices and critical bio-medical applications as compared to artificial neural networks (ANNs).…
We propose a definition of {\it security} and a definition of {\it robustness} of networks against the cascading failure models of deliberate attacks and random errors respectively, and investigate the principles of the security and…
With the explosion of the public Internet and e-commerce, private computers, and computer networks, if not adequately secured, are increasingly vulnerable to damaging attacks. Hackers, viruses, vindictive employees and even human error all…
Ride Hailing Services (RHS) have become a popular means of transportation, and with its popularity comes the concerns of privacy of riders and drivers. ORide is a privacy-preserving RHS proposed at the USENIX Security Symposium 2017 and…
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in various graph-based learning tasks. However, enabling privacy-preserving GNNs in encrypted domains, such as under Fully Homomorphic Encryption (FHE), typically…
Advancements in deep learning enable cloud servers to provide inference-as-a-service for clients. In this scenario, clients send their raw data to the server to run the deep learning model and send back the results. One standing challenge…
The privacy-preserving federated learning schemes based on the setting of two honest-but-curious and non-colluding servers offer promising solutions in terms of security and efficiency. However, our investigation reveals that these schemes…
With changes in privacy laws, there is often a hard requirement for client data to remain on the device rather than being sent to the server. Therefore, most processing happens on the device, and only an altered element is sent to the…