Advancements and challenges in next-generation cryptographic techniques: A security and performance perspective

To tackle emerging security and privacy concerns in consortium blockchain applications, particularly in handling sensitive identity information and transaction data, a novel privacy-preserving framework is proposed. The scheme leverages advanced technologies such as post-quantum cryptography, multi-party computation (MPC), and fully homomorphic encryption (FHE) to enhance security and efficiency. Firstly, a quantum-resistant blind signature mechanism is designed using lattice-based cryptography to mitigate centralization issues and strengthen security against quantum attacks. Secondly, a regulatory-compliant hierarchical encryption model is developed using fully homomorphic encryption, enabling fine-grained access control and data integrity verification without decrypting sensitive data. Additionally, zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) are integrated to ensure privacy while allowing verifiable computation. Experimental evaluations demonstrate that the proposed scheme achieves high encryption efficiency, reduced computational overhead during encryption/decryption, and strong resistance to malicious activities such as tampering, eavesdropping, and replay attacks

Motivated by new applications such as secure Multi-Party Computation (MPC), Fully Homomorphic Encryption (FHE), and Zero-Knowledge proofs (ZK), many MPC-, FHE- and ZK-friendly symmetric-key primitives that minimize the< number of multiplications over Fp for a large prime p have been recently proposed in the literature. These symmetric primitives are usually defined via invertible functions, including (i) Feistel and Lai-Massey schemes and (ii) SPN constructions instantiated with invertible non-linear S-Boxes. However, the “invertibility” property is actually never required in any of the mentioned applications.In this paper, we discuss the possibility to set up MPC-/FHE-/ZK-friendly symmetric primitives instantiated with non-invertible bounded surjective functions. In contrast to one-to-one functions, each output of a l-bounded surjective function admits at most l pre-images. The simplest example is the square map x → x2 over Fp for a prime p ≥ 3, which is (obviously) 2-bounded surjective. When working over Fnp for n ≥ 2, we set up bounded surjective functions by re-considering the recent results proposed by Grassi, Onofri, Pedicini and Sozzi at FSE/ToSC 2022 as starting points. Given a quadratic local map F : Fmp → Fp for m ∈ {1, 2, 3}, they proved that the shift-invariant non-linear function over Fnp defined as SF (x0, x1, . . . , xn−1) = y0∥y1∥ . . . ∥yn−1 where yi := F(xi, xi+1) is never invertible for any n ≥ 2 · m − 1. Here, we prove that • the quadratic function F : Fmp → Fp for m ∈ {1, 2} that minimizes the probability of having a collision for SF over Fnp is of the form F(x0, x1) = x20 + x1 (or equivalent);• the function SF over Fnp defined as before via F(x0, x1) = x20 +x1 (or equivalent) is 2n-bounded surjective.As concrete applications, we propose modified versions of the MPC-friendly schemes MiMC, HadesMiMC, and (partially of) Hydra, and of the FHE-friendly schemes Masta, Pasta, and Rubato. By instantiating them with the bounded surjective quadratic functions proposed in this paper, we are able to improve the security and/or the performances in the target applications/protocols.

Fully homomorphic encryption (FHE) allows computing on encrypted data, enabling secure offloading of computation to untrusted servers. Though it provides ideal security, FHE is prohibitively expensive when executed in software. These overheads are a major barrier to FHE's widespread adoption. We present F1, the first FHE accelerator that is capable of executing full FHE programs. F1 builds on an in-depth architectural analysis of the characteristics of FHE computations that reveals acceleration opportunities. F1 is a wide-vector processor with novel functional units deeply specialized to FHE primitives, such as modular arithmetic, number-theoretic transforms, and structured permutations. This organization provides so much compute throughput that data movement becomes the bottleneck. Thus, F1 is primarily designed to minimize data movement. F1 is the first system to accelerate complete FHE programs, and outperforms state-of-the-art software implementations by gmean 5,400x. These speedups counter FHE's overheads and enable new applications, like real-time private deep learning in the cloud.

We present new constructions of multi-party homomorphic secret sharing (HSS) based on a new primitive that we call homomorphic encryption with decryption to shares (HEDS). Our first construction, which we call \(\mathsf {Scooby} \), is based on many popular fully homomorphic encryption (FHE) schemes with a linear decryption property. \(\mathsf {Scooby} \) achieves an n-party HSS for general circuits with complexity \(O(|F| + \log n)\), as opposed to \(O(n^2 \cdot |F|)\) for the prior best construction based on multi-key FHE. \(\mathsf {Scooby} \) can be based on (ring)-LWE with a super-polynomial modulus-to-noise ratio. In our second construction, \(\mathsf {Scrappy} \), assuming any generic FHE plus HSS for NC1-circuits, we obtain a HEDS scheme which does not require a super-polynomial modulus. While these schemes all require FHE, in another instantiation, \(\mathsf {Shaggy} \), we show how in some cases it is possible to obtain multi-party HSS without FHE, for a small number of parties and constant-degree polynomials. Finally, we show that our \(\mathsf {Scooby} \) scheme can be adapted to use multi-key fully homomorphic encryption, giving more efficient spooky encryption and setup-free HSS. This latter scheme, \(\mathsf {Casper} \), if concretely instantiated with a B/FV-style multi-key FHE scheme, for functions F which do not require bootstrapping, gives an HSS complexity of \(O(n \cdot |F| + n^2 \cdot \log n)\).

Modern cryptography pushed forward the need of having provable security. Whereas ancient cryptography was only relying on heuristic assumptions and the secrecy of the designs, nowadays researchers try to make the security of schemes to rely on mathematical problems which are believed hard to solve. When doing these proofs, the capabilities of potential adversaries are modeled formally. For instance, the black-box model assumes that an adversary does not learn anything from the inner-state of a construction. While this assumption makes sense in some practical scenarios, it was shown that one can sometimes learn some information by other means, e.g., by timing how long the computation take. In this thesis, we focus on two different areas of cryptography. In both parts, we take first a theoretical point of view to obtain a result. We try then to adapt our results so that they are easily usable for implementers and for researchers working in practical cryptography. In the first part of this thesis, we take a look at post-quantum cryptography, i.e., at cryptographic primitives that are believed secure even in the case (reasonably big) quantum computers are built. We introduce HELEN, a new public-key cryptosystem based on the hardness of the learning from parity with noise problem (LPN). To make our results more concrete, we suggest some practical instances which make the system easily implementable. As stated above, the design of cryptographic primitives usually relies on some well-studied hard problems. However, to suggest concrete parameters for these primitives, one needs to know the precise complexity of algorithms solving the underlying hard problem. In this thesis, we focus on two recent hard-problems that became very popular in post-quantum cryptography: the learning with error (LWE) and the learning with rounding problem (LWR). We introduce a new algorithm that solves both problems and provide a careful complexity analysis so that these problems can be used to construct practical cryptographic primitives. In the second part, we look at leakage-resilient cryptography which studies adversaries able to get some side-channel information from a cryptographic primitive. In the past, two main disjoint models were considered. The first one, the threshold probing model, assumes that the adversary can put a limited number of probes in a circuit. He then learns all the values going through these probes. This model was used mostly by theoreticians as it allows very elegant and convenient proofs. The second model, the noisy-leakage model, assumes that every component of the circuit leaks but that the observed signal is noisy. Typically, some Gaussian noise is added to it. According to experiments, this model depicts closely the real behaviour of circuits. Hence, this model is cherished by the practical cryptographic community. In this thesis, we show that making a proof in the first model implies a proof in the second model which unifies the two models and reconciles both communities. We then look at this result with a more practical point-of-view. We show how it can help in the process of evaluating the security of a chip based solely on the more standard mutual information metric.

Secure report is the problem of a client that retrieves all records matching specified attributes from a database table at the server (e.g. cloud), as in SQL SELECT queries, but where the query and the database are encrypted. Here, only the client has the secret key, but still the server is expected to compute and return the encrypted result. Secure report is theoretically possible with Fully Homomorphic Encryption (FHE). However, the current state-of-the-art solutions are realized by a polynomial of degree that is at least linear in the number m of records, which is too slow in practice even for very small databases. We present the first solution that is realized by a polynomial that attains degree independent of the number of records m, as well as the first implementation of an FHE solution to Secure report. This is by suggesting a novel paradigm that forges a link between cryptography and modern data summarization techniques known as coresets (core-sets), and sketches in particular. The key idea is to compute only a coreset of the desired report. Since the coreset is small, the client can quickly decode the desired report that the server computes after decrypting the coreset. We implemented our main reporting system in an open source library. This is the first implemented system that can answer such database queries when processing only FHE encrypted data and queries. As our analysis promises, the experimental results show that we can run Secure report queries on billions records in minutes on an Amazon EC2 server, compared to less than a hundred-thousands in previous FHE based solutions.

SummaryThe present scenario witnesses “the second quantum revolution,” which has enabled the development of revolutionary novel quantum tools. Quantum computing endeavors to establish higher computing standards that can potentially solve complex structures. Post‐quantum cryptography (PQC) has emerged as one of the new domains of cryptography, which is resilient to quantum attacks owing to the revolution in quantum computing. To ensure quantum security, the lattice‐based cryptosystem (LB‐cryptosystem) is one of the promising tools of PQC to address quantum‐based threats. The traditional security algorithms, such as RSA and Diffie–Hellman (DH), are strong enough to resist present security threats. However, it has been predicted that quantum technologies have the capability to break the security of most traditional algorithms whose security is based on prime factorization and DH‐type hard problems. Therefore, research is currently focused on addressing the security and privacy threats by using LB‐cryptosystems to secure various applications, organizations' data, and information infrastructure in the quantum era. The purpose of this article is to investigate recent advances in LB‐cryptosystems that may allow the design of secure models for smart‐grid networks (SGNs) against existing and future quantum attacks. SGNs have been explored as the bi‐directional assimilation of communication in terms of electricity generation, transmission, allocation, and utilization. This survey provides a comprehensive overview of LB‐cryptosystems, as well as their potential applications in securing SGNs. Lastly, the article summarizes the various PQC primitives, NIST selected algorithms, open‐source tools along with their packages, and various PQC industrial initiatives, and also compares traditional cryptographic schemes with other PQC.

Fully Homomorphic Encryption (FHE) allows a client to share their data with an external server without ever exposing their data. FHE serves as a potential solution for data breaches and the marketing of users’ private data. Unfortunately, FHE is much slower than conventional asymmetric cryptography, where data are encrypted only between endpoints. Within this work, we propose the Dynamic AcceleRaTor for Parallel Homomorphic pROGrams, DARTPHROG, as a potential tool for accelerating FHE. DARTPHROG is a superscalar architecture, allowing multiple homomorphic operations to be executed in parallel. Furthermore, DARTPHROG is the first to utilize the new Hardware Optimized Modular-Reduction (HOM-R) system, showcasing the uniquely efficient method compared to Barrett and Montgomery reduction. Coming in at 40.5 W, DARTPHROG is one of the smaller architectures for FHE acceleration. Our architecture offers speedups of up to 1860 times for primitive FHE operations such as ciphertext/plaintext and ciphertext/ciphertext addition, subtraction, and multiplication when operations are performed in parallel using the superscalar feature in DARTPHROG. The DARTPHROG system implements an assembler, a unique instruction set based on THUMB, and a homomorphic processor implemented on a Field Programmable Gate Array (FPGA). DARTPHROG is also the first superscalar evaluation of homomorphic operations when the Number Theoretic Transform (NTT) is excluded from the design. Our processor can therefore be used as a base case for evaluation when weighing the resource and execution impact of NTT implementations.

Fully homomorphic encryption (FHE) enables privacy-preserving machine learning (PPML) at the cost of intensive computational overhead, which necessitates the use of domain-specific accelerators. To achieve comprehensive support for leveled FHE, this article presents a reconfigurable multi-scheme FHE processor that supports both client-side encryption/decryption and server-side evaluation. First, a reconfigurable processing element (RPE) design for modular arithmetic and a reusable data generator for polynomial sampling are developed to support the various operations in FHE. Second, a configurable RPE array supporting polynomial operations and a decoupled automorphism unit (DAU) necessary for homomorphic rotations are proposed to accelerate the FHE primitives with complex dataflow. Finally, an on-chip data generation strategy and a cache-aware operation scheduling (CAOS) method are introduced to alleviate the memory bottleneck in the end-to-end execution of FHE applications. The chip is fabricated in a 28-nm process and tested with end-to-end execution. Targeting a lightweight parameter set with polynomial degree<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N=4096$</tex-math> </inline-formula> at 128-bit security level, the proposed chip achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.05~\mu $</tex-math> </inline-formula>J per encryption on the client side and provides a throughput of 8.72 kHMul/s on the server side. In terms of the number theory transform (NTT) operation, the chip demonstrates the highest throughput and best area efficiency compared with state-of-the-art solutions.

Fully Homomorphic Encryption (FHE) is the holy grail of encrypted communications. It opens the door to several advanced functionalities to overcome the security and trust issues of the IT world. After 2009, once Craig Gentry had shown that FHE could be achieved, a study in this field boomed, and significant improvement was made in identifying more efficient and realistic programs. FHE is primitive cryptography that enables arbitrary functions to be calculated via encrypted data. These systems are applicable in different ways since they permit users to encrypt their private information securely while still outsourcing the processing of protected data without fearing disclosing the real data. In 2012, LTV12 presented the first multi-key FHE system and demonstrated the possibility of using multi-key systems in somewhat homomorphic encryption (SHE). Like in the one key context, there have been many advances in the field, but no effort has been made to develop the multi-key methods. This paper presents a discussion of FHE and MKFHE with a specific focus on the current techniques and three implementations, comprising the first in the multi-key setups, to the extent of our understanding.

Accelerating the development of a practical Fully Homomorphic Encryption (FHE) scheme is the goal of the DARPA PROCEED program. For the past year, this program has had as its focus the acceleration of various aspects of the FHE concept toward practical implementation and use. FHE would be a game-changing technology to enable secure, general computation on encrypted data, e.g., on untrusted off-site hardware. However, FHE will still require several orders of magnitude improvement in computation before it will be practical for widespread use. Recent theoretical breakthroughs demonstrated the existence of FHE schemes [1, 2], and to date much progress has been made in both algorithmic and implementation improvements. Specifically our contribution to the Proceed program has been the development of FPGA based hardware primitives to accelerate the computation on encrypted data using FHE based on lattice techniques [3]. Our project, SIPHER, has been using a state of the art tool-chain developed by Mathworks to implement VHDL code for FPGA circuits directly from Simulink models. Our baseline Homomorphic Encryption prototypes are developed directly in Matlab using the fixed point toolbox to perform the required integer arithmetic. Constant improvements in algorithms require us to be able to quickly implement them in a high level language such as Matlab. We reported on our initial results at HPEC 2011 [4]. In the past year, increases in algorithm complexity have introduced several new design requirements for our FPGA implementation. This report presents new Simulink primitives that had to be developed to deal with these new requirements.

Recent developments in quantum computing have shed light on the shortcomings of the conventional public cryptosystem. Even while Shor’s algorithm cannot yet be implemented on quantum computers, it indicates that asymmetric key encryption will not be practicable or secure in the near future. The NIST has started looking for a post-quantum encryption algorithm that is resistant to the development of future quantum computers as a response to this security concern. The current focus is on standardizing asymmetric cryptography that should be impenetrable by a quantum computer. This has become increasingly important in recent years. Currently, the process of standardizing asymmetric cryptography is coming very close to being finished. This study evaluated the performance of two PQC algorithms, both of which were selected as NIST fourth-round finalists. The research assessed the key generation, encapsulation, and decapsulation operations, providing insights into their efficiency and suitability for real-world applications. Further research and standardization efforts are required to enable secure and efficient post-quantum encryption. When selecting appropriate post-quantum encryption algorithms for specific applications, factors such as security levels, performance requirements, key sizes, and platform compatibility should be taken into account. This paper provides helpful insight for post-quantum cryptography researchers and practitioners, assisting in the decision-making process for selecting appropriate algorithms to protect confidential data in the age of quantum computing.

The proliferation of Internet of Things (IoT) devices and their vulnerability to malware infections pose critical security challenges in IoT networks and multi-access edge computing (MEC). Traditional federated learning-based IoT malware detection (FL-IMD) methods face limitations in privacy, reliability, and client authentication, necessitating innovative solutions. This study proposes a reliable and privacy-preserving federated learning framework (RPFL) that integrates elliptic curve digital signature algorithm (ECDSA), homomorphic encryption and blockchain technology to enhance privacy, reliability, and client verification in FL-IMD. To address challenges with fully homomorphic encryption (FHE), particularly its reliance on an external aggregator, we introduce two smart contract-based schemes: one to incentivize client participation and another to mitigate aggregator failures. Experimental results on the N-BaIoT dataset show that RPFL achieves IoT malware detection accuracy comparable to state-of-the-art methods, while significantly enhancing reliability and privacy in the aggregation process. Furthermore, our blockchain integration outperforms the prominent blockchain-based FL framework, BCFL, by reducing communication costs and latency. These findings highlight the potential of RPFL to advance privacy-preserving, reliable, and secure FL-based IMD in IoT networks and MEC environments.

A roadmap from classical cryptography to post-quantum resistant cryptography for 5G-enabled IoT: Challenges, opportunities and solutions

The rapid expansion of the Internet of Things (IoT) has introduced unprecedented cybersecurity challenges, as traditional cryptographic methods struggle to provide both efficiency and quantum resistance in resource-constrained environments. This research introduces Adaptive Quantum Cryptography (AQC), a novel security framework that integrates Quantum Key Distribution (QKD), Post-Quantum Cryptography (PQC), Quantum Random Number Generators (QRNGs), and AI-driven dynamic adaptation to safeguard IoT networks against evolving cyber threats. Unlike classical encryption techniques such as RSA and ECC, which are vulnerable to quantum attacks, AQC leverages quantum mechanical principles—such as superposition, entanglement, and the no-cloning theorem—to ensure provably secure key exchange and data encryption. The proposed AQC system dynamically adjusts cryptographic protocols based on real-time network conditions, computational resources, and threat intelligence, optimizing security without imposing excessive overhead. Experimental evaluations demonstrate that AQC achieves a 70% improvement in key exchange efficiency, a 50% reduction in computational load, and a 95% resilience rate against quantum attacks compared to conventional encryption methods. Additionally, the integration of QRNGs enhances cryptographic key entropy, while AI-driven anomaly detection enables proactive threat mitigation. This framework is particularly suited for next-generation IoT applications, including smart grids, autonomous vehicles, and industrial automation, where security, scalability, and energy efficiency are critical. By combining quantum-resistant algorithms with adaptive security mechanisms, AQC provides forward secrecy, resistance to quantum adversaries, and robust authentication, ensuring long-term protection for IoT ecosystems in the post-quantum era. The findings underscore AQC’s potential as a foundational security architecture for future quantum-safe IoT networks.