Artificial Intelligence as a Service (AIaaS) enables users to query a model hosted by a service provider and receive inference results from a pre-trained model. Although AIaaS makes artificial intelligence more accessible, particularly for resource-limited users, it also raises verifiability and privacy concerns for the client and server, respectively. While zero-knowledge proof techniques can address these concerns simultaneously, they incur high proving costs due to the non-linear operations involved in AI inference and suffer from precision loss because they rely on fixed-point representations to model real numbers.

Federated learning (FL) is well-suited to 5G networks, where many mobile devices generate sensitive edge data. Secure aggregation protocols enhance privacy in FL by ensuring that individual user updates reveal no information about the underlying client data. However, the dynamic and large-scale nature of 5G-marked by high mobility and frequent dropouts-poses significant challenges to the effective adoption of these protocols. Existing protocols often require multi-round communication or rely on fixed infrastructure, limiting their practicality. We propose a lightweight, single-round secure aggregation protocol designed for 5G environments. By leveraging base stations for assisted computation and incorporating precomputation, key-homomorphic pseudorandom functions, and t-out-of-k secret sharing, our protocol ensures efficiency, robustness, and privacy. Experiments show strong security guarantees and significant gains in communication and computation efficiency, making the approach well-suited for real-world 5G FL deployments.

Federated learning (FL) enables collaborative model training while preserving user data privacy by keeping data local. Despite these advantages, FL remains vulnerable to privacy attacks on user updates and model parameters during training and deployment. Secure aggregation protocols have been proposed to protect user updates by encrypting them, but these methods often incur high computational costs and are not resistant to quantum computers. Additionally, differential privacy (DP) has been used to mitigate privacy leakages, but existing methods focus on secure aggregation or DP, neglecting their potential synergies. To address these gaps, we introduce$\texttt {Beskar}$, a novel framework that provides post-quantum secure aggregation, optimizes computational overhead for FL settings, and defines a comprehensive threat model that accounts for a wide spectrum of adversaries. We also integrate DP into different stages of FL training to enhance privacy protection in diverse scenarios. Our framework provides a detailed analysis of the trade-offs between security, performance, and model accuracy, representing the first thorough examination of secure aggregation protocols combined with various DP approaches for post-quantum secure FL.$\texttt {Beskar}$aims to address the pressing privacy and security issues FL while ensuring quantum-safety and robust performance.

Secure aggregation protocols ensure the privacy of users’ data in federated learning by preventing the disclosure of local gradients. Many existing protocols impose significant communication and computational burdens on participants and may not efficiently handle the large update vectors typical of machine learning models. Correspondingly, we present e-SeaFL, an efficient verifiable secure aggregation protocol taking only one communication round during the aggregation phase. e-SeaFL allows the aggregation server to generate proof of honest aggregation to participants via authenticated homomorphic vector commitments. Our core idea is the use of assisting nodes to help the aggregation server, under similar trust assumptions existing works place upon the participating users. Our experiments show that the user enjoys an order of magnitude efficiency improvement over the state-of-the-art (IEEE S&P 2023) for large gradient vectors with thousands of parameters. Our open-source implementation is available at https://github.com/vt-asaplab/e-SeaFL.

Differentially private stochastic gradient descent (DP-SGD) has been instrumental in privately training deep learning models by providing a framework to control and track the privacy loss incurred during training. At the core of this computation lies a subsampling method that uses a privacy amplification lemma to enhance the privacy guarantees provided by the additive noise. Fixed size subsampling is appealing for its constant memory usage, unlike the variable sized minibatches in Poisson subsampling. It is also of interest in addressing class imbalance and federated learning. Current computable guarantees for fixed-size subsampling are not tight and do not consider both add/remove and replace-one adjacency relationships. We present a new and holistic Rényi differential privacy (RDP) accountant for DP-SGD with fixed-size subsampling without replacement (FSwoR) and with replacement (FSwR). For FSwoR we consider both add/remove and replace-one adjacency, where we improve on the best current computable bound by a factor of $4$. We also show for the first time that the widely-used Poisson subsampling and FSwoR with replace-one adjacency have the same privacy to leading order in the sampling probability. Our work suggests that FSwoR is often preferable to Poisson subsampling due to constant memory usage. Our FSwR accountant includes explicit non-asymptotic upper and lower bounds and, to the authors' knowledge, is the first such RDP analysis of fixed-size subsampling with replacement for DP-SGD. We analytically and empirically compare fixed size and Poisson subsampling, and show that DP-SGD gradients in a fixed-size subsampling regime exhibit lower variance in practice in addition to memory usage benefits.

A digital signature is an essential cryptographic tool to offer authentication with public verifiability, non-repudiation, and scalability. However, digital signatures often rely on expensive operations that can be highly costly for low-end devices, typically seen in the Internet of Things and Systems (IoTs). These efficiency concerns especially deepen when post-quantum secure digital signatures are considered. Hence, it is of vital importance to devise post-quantum secure digital signatures that are designed with the needs of such constraint IoT systems in mind.

The lack of authentication protection for bootstrapping messages broadcast by base-stations makes impossible for devices to differentiate between a legitimate and a fake base-station. This vulnerability has been widely acknowledged, but not yet fixed and thus enables law-enforcement agencies, motivated adversaries and nation-states to carry out attacks against targeted users. Although 5G cellular protocols have been enhanced to prevent some of these attacks, the root vulnerability for fake base-stations still exists. In this paper, we propose an efficient broadcast authentication protocol based on a hierarchical identity-based signature scheme, Schnorr-HIBS, which addresses the root cause of the fake base-station problem with minimal computation and communication overhead. We implement and evaluate our proposed protocol using off-the-shelf software-defined radios and open-source libraries. We also provide a comprehensive quantitative and qualitative comparison between our scheme and other candidate solutions for 5G base-station authentication proposed by 3GPP. Our proposed protocol achieves at least a 6x speedup in terms of end-to-end cryptographic delay and a communication cost reduction of 31% over other 3GPP proposals.

Multi-user oblivious storage allows users to access their shared data on the cloud while retaining access pattern obliviousness and data confidentiality simultaneously. Most secure and efficient oblivious storage systems focus on the utilization of the maximum network bandwidth in serving concurrent accesses via a trusted proxy. How- ever, since the proxy executes a standard ORAM protocol over the network, the performance is capped by the network bandwidth and latency. Moreover, some important features such as access control and security against active adversaries have not been thoroughly explored in such proxy settings. In this paper, we propose MOSE, a multi-user oblivious storage system that is efficient and enjoys from some desirable security properties. Our main idea is to harness a secure enclave, namely Intel SGX, residing on the untrusted storage server to execute proxy logic, thereby, minimizing the network bottleneck of proxy-based designs. In this regard, we address various technical design chal- lenges such as memory constraints, side-channel attacks and scala- bility issues when enabling proxy logic in the secure enclave. We present a formal security model and analysis for secure enclave multi-user ORAM with access control. We optimize MOSE to boost its throughput in serving concurrent requests. We implemented MOSE and evaluated its performance on commodity hardware. Our evaluation confirmed the efficiency of MOSE, where it achieves approximately two orders of magnitudes higher throughput than the state-of-the-art proxy-based design, and also, its performance is scalable proportional to the available system resources.

Public key Encryption with Keyword Search (PEKS) aims in mitigating the impacts of data privacy versus utilization dilemma by allowing any user in the system to send encrypted files to the server to be searched by a receiver. The receiver can retrieve the encrypted files containing specific keywords by providing the corresponding trapdoors of these keywords to the server. Despite their merits, the existing PEKS schemes introduce a high end-to-end delay that may hinder their adoption in practice. Moreover, they do not scale well for large security parameters and provide no post-quantum security promises. In this paper, we propose two novel lattice-based PEKS schemes that offer a high computational efficiency along with better security assurances than that of the existing alternatives. Specifically, our NTRU-PEKS scheme achieves 18 times lower end-to-end delay than the most efficient pairing-based alternatives. Our LWE-PEKS offers provable security in the standard model with a reduction to the worst-case lattice problems. We fully implemented our NTRU-PEKS scheme and benchmarked its performance as deployed on Amazon Web Services cloud infrastructures.

Efficient authentication is vital for IoT applications with stringent minimum-delay requirements (e.g., energy delivery systems). This requirement becomes even more crucial when the IoT devices are battery-powered, like small aerial drones, and the efficiency of authentication directly translates to more operation time. Although some fast authentication techniques have been proposed, some of them might not fully meet the needs of the emerging delay-aware IoT. In this paper, we propose a new signature scheme called ARIS that pushes the limits of the existing digital signatures, wherein a commodity hardware can verify 83,333 signatures per second. ARIS also enables the fastest signature generation along with the lowest energy consumption and end-to-end delay among its counterparts. These significant computational advantages come with a larger storage requirement, which is a favorable trade-off for some critical delay-aware applications. These desirable features are achieved by harnessing message encoding with cover-free families and a special elliptic curve based oneway function. We prove the security of ARIS under the hardness of the elliptic curve discrete logarithm problem in the random oracle model. We provide an open-sourced implementation of ARIS on commodity hardware and 8-bit AVR microcontroller for public testing and verification.

We introduce a simple, yet efficient digital signature scheme which offers post-quantum security promise. Our scheme, named TACHYON, is based on a novel approach for extending one-time hash-based signatures to (polynomially bounded) many-time signatures, using the additively homomorphic properties of generalized compact knapsack functions. Our design permits TACHYON~to achieve several key properties. First, its signing and verification algorithms are the fastest among its current counterparts with a higher level of security. This allows TACHYON~to achieve the lowest end-to-end delay among its counterparts, while also making it suitable for resource-limited signers. Second, its private keys can be as small as κ bits, where κ is the desired security level. Third, unlike most of its lattice-based counterparts, TACHYON~does not require any Gaussian sampling during signing, and therefore, is free from side-channel attacks targeting this process. We also explore various speed and storage trade-offs for TACHYON, thanks to its highly tunable parameters. Some of these trade-offs can speed up TACHYON signing in exchange for larger keys, thereby permitting TACHYON~to further improve its end-to-end delay.

Duan proposed the first certificateless undeniable signature scheme in 2008. Later in 2012, Zhao and Ye proffered an efficient scheme which enjoys from a pairing-free sign algorithm. In this paper, we prove the insecurity of their efficient scheme by mounting two attacks on its invisibility and non-impersonation. In addition, we propose an improved scheme that addresses both of the above attacks while providing better flexibility and additional features for the signer.

ABSTRACT In 2013, Dehkordi and Alimoradi proposed a certificateless identification scheme using supersingular elliptic curves. This proposal came independent of the parallel work of Chin et al. in proposing the first known security models for certificateless identification with provable security. In this paper, we show that there are some design flaws in the Dehkordi–Alimoradi scheme, which lead one to conclude that their scheme is insecure. Copyright © 2014 John Wiley & Sons, Ltd.