Privacy-preserving computation meets quantum computing: A scoping review

BackgroundThe collection, storage, and analysis of large data sets are relevant in many sectors. Especially in the medical field, the processing of patient data promises great progress in personalized health care. However, it is strictly regulated, such as by the General Data Protection Regulation (GDPR). These regulations mandate strict data security and data protection and, thus, create major challenges for collecting and using large data sets. Technologies such as federated learning (FL), especially paired with differential privacy (DP) and secure multiparty computation (SMPC), aim to solve these challenges.ObjectiveThis scoping review aimed to summarize the current discussion on the legal questions and concerns related to FL systems in medical research. We were particularly interested in whether and to what extent FL applications and training processes are compliant with the GDPR data protection law and whether the use of the aforementioned privacy-enhancing technologies (DP and SMPC) affects this legal compliance. We placed special emphasis on the consequences for medical research and development.MethodsWe performed a scoping review according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). We reviewed articles on Beck-Online, SSRN, ScienceDirect, arXiv, and Google Scholar published in German or English between 2016 and 2022. We examined 4 questions: whether local and global models are “personal data” as per the GDPR; what the “roles” as defined by the GDPR of various parties in FL are; who controls the data at various stages of the training process; and how, if at all, the use of privacy-enhancing technologies affects these findings.ResultsWe identified and summarized the findings of 56 relevant publications on FL. Local and likely also global models constitute personal data according to the GDPR. FL strengthens data protection but is still vulnerable to a number of attacks and the possibility of data leakage. These concerns can be successfully addressed through the privacy-enhancing technologies SMPC and DP.ConclusionsCombining FL with SMPC and DP is necessary to fulfill the legal data protection requirements (GDPR) in medical research dealing with personal data. Even though some technical and legal challenges remain, for example, the possibility of successful attacks on the system, combining FL with SMPC and DP creates enough security to satisfy the legal requirements of the GDPR. This combination thereby provides an attractive technical solution for health institutions willing to collaborate without exposing their data to risk. From a legal perspective, the combination provides enough built-in security measures to satisfy data protection requirements, and from a technical perspective, the combination provides secure systems with comparable performance with centralized machine learning applications.

BACKGROUND This study aims to explore the role of programming languages in the development and implementation of mathematical models, with a focus on the integration of advanced computing technologies. MATERIALS AND METHODS Utilising a narrative review method, the study methodically examines the body of research on mathematical modelling and the use of programming languages like Python, C++, and Julia. The performance of these languages is compared in a number of mathematical modelling tasks, such as numerical methods, linear algebra, and physical modelling. RESULTS The paper emphasises how cloud computing, artificial intelligence, and hybrid algorithms have significantly improved the precision and effectiveness of mathematical models. While C++ offers great performance in computationally demanding jobs but necessitates more development effort, Python has been demonstrated to be beneficial for speedy development because of its vast library ecosystem. Julia is a promising language for mathematical modelling because it strikes a compromise between usability and performance. The investigation also shows that the choice of computing methods and programming languages has a significant impact on the effectiveness of mathematical models. Every language offers advantages based on the particular modelling task, as shown by a thorough analysis of execution time, memory utilisation, and code size. Furthermore, the combination of quantum computing and machine learning offers fresh possibilities for resolving increasingly challenging issues that conventional approaches are unable to effectively handle. CONCLUSION According to the study’s findings, mathematical modelling will depend more and more on the cooperation of traditional approaches, contemporary programming languages, and cutting-edge technologies like artificial intelligence and quantum computing.

Quantum computing innovations have garnered significant attention for their potential to revolutionize industries, with the energy sector being one of the most promising areas for application. As global energy demand increases and sustainability becomes more critical, computational technologies offer groundbreaking solutions for energy production, storage, and distribution. In this landscape, quantum computing plays a crucial role in unlocking the full potential of artificial intelligence and machine learning as research and development in the quantum machine learning field grows constantly. We here present a scoping review of early quantum machine learning applications within the energy industry value chain. Starting from 34 sources, we analyze and discuss 22 use cases in the energy sector, thoroughly examining each to understand its potential applications and impact. We then evaluate these early-stage quantum applications to determine their feasibility and benefits, offering insights into their relevance and effectiveness in the context of the industry’s evolving landscape. This is done by introducing a novel framework: the Assessment Model for Innovation Management (AMIM). Our research highlights the opportunities that quantum innovations present for the energy sector and offers actionable insights into which applications are the best investments and why. Overall, the feasibility and technological maturity of quantum machine learning use cases are still in the early stages, though their market compatibility and potential benefits are mostly relatively high. This indicates that while quantum machine learning holds immense potential, further development is necessary to fully realize its benefits in the energy sector.

Secret sharing and multiparty computation (also called "secure function evaluation") are fundamental primitives in modern cryptography, allowing a group of mutually distrustful players to perform correct, distributed computations under the sole assumption that some number of them will follow the protocol honestly. This paper investigates how much trust is necessary -- that is, how many players must remain honest -- in order for distributed quantum computations to be possible. We present a verifiable quantum secret sharing (VQSS) protocol, and a general secure multiparty quantum computation (MPQC) protocol, which can tolerate any (n-1)/2 (rounded down) cheaters among n players. Previous protocols for these tasks tolerated (n-1)/4 (rounded down) and (n-1)/6 (rounded down) cheaters, respectively. The threshold we achieve is tight - even in the classical case, ``fair'' multiparty computation is not possible if any set of n/2 players can cheat. Our protocols rely on approximate quantum error-correcting codes, which can tolerate a larger fraction of errors than traditional, exact codes. We introduce new families of authentication schemes and approximate codes tailored to the needs of our protocols, as well as new state purification techniques along the lines of those used in fault-tolerant quantum circuits.

The objective of this research is to explore the integration of quantum computing with multi-cloud architectures, aiming to enhance computational efficiency and security in advanced cloud environments. The study seeks to identify the potential benefits and challenges of incorporating quantum computing capabilities within a multi-cloud framework and to evaluate the impact on performance and security metrics. The research employs a hybrid methodological approach, combining both theoretical analysis and practical implementation. Initially, a detailed literature review is conducted to understand the current state of quantum computing and multi-cloud architectures. This is followed by the design and development of an integration framework that leverages quantum computing technologies in a multi-cloud environment. Key steps include developing a multi-cloud architecture that integrates quantum computing resources alongside classical computing resources, deploying quantum algorithms and protocols within the multi-cloud setup, implementing advanced security measures to protect data and computational processes, using a set of predefined metrics to evaluate computational efficiency and security, and employing statistical tools and techniques to analyze the collected data and draw meaningful insights. The integration of quantum computing with multi-cloud architectures resulted in significant improvements in computational efficiency, particularly in tasks that are traditionally resource-intensive. Key findings include enhanced computational speed, where quantum algorithms demonstrated superior performance in solving complex problems compared to classical algorithms, optimized resource utilization through dynamic allocation of quantum and classical resources leading to cost efficiency, improved security with quantum-enhanced protocols providing robust protection against cyber threats, and high scalability of the integrated architecture to accommodate increasing computational demands without compromising performance. The research concludes that integrating quantum computing with multi-cloud architectures offers substantial benefits in terms of computational efficiency and security. The findings indicate that such integration can revolutionize cloud computing, providing a powerful platform for handling complex computations and enhancing data security. However, the study also highlights several challenges, including the need for specialized hardware, the complexity of integration, and the necessity for ongoing research to fully harness the potential of quantum computing in cloud environments. Future research should focus on addressing these challenges and exploring further applications of quantum computing in various cloud-based scenarios.

Secure multi-party computation (SMPC) protocols allow several parties distrusting each other to collectively compute a function on their inputs, without revealing the input values. In this paper, we introduce a protocol that lifts SMPC to its quantum counterpart—secure multi-party quantum computation (SMPQC) for classical inputs and outputs—in a composable and statistically secure way, even for a single honest party. The soundness error—the maximum cheating probability of malicious parties—is shown to be proportional to the inverse of a polynomial with respect to the number of rounds in the protocol, and can be further decreased to a negligible quantity for bounded-error quantum-polynomial-time computations. Unlike previous SMPQC protocols, our proposal only requires very limited quantum resources from all but one party. In addition, the protocol exhibits some noise robustness that can facilitate small-scale implementations with near-future technologies. The protocol is based on a new technique for quantum verification that requires only the collective remote preparation of quantum states in a single plane of the Bloch sphere. To demonstrate the ability of verifying the computation with such limited clients, we uncover and use a fundamental invariance that is inherent to measurement-based quantum computing.

Cloud Computing (CC) and Quantum Computing, both have been interesting areas of research, individually. However, integrating the two can come with mutual benefits for both the fields. Cloud platforms can offer Quantum Computing as a service, but more importantly, they are capable of offering the flexibility, inherent in architecture, to accommodate new developments in Quantum Computing. On the other hand, Quantum Computing in general and Quantum Cryptography (QC), in particular, can help in alleviating the security concerns associated with CC, which have prevented users from migrating to cloud. The paper proposes a model framework contemplating the use of Blind Quantum Computation (BQC) between cloud servers involved in multiparty computations during online phase and using authenticated Quantum Key Distribution (QKD) for secure distribution of keys (used for encrypting the secret files) when going into offline phase. Finally, a proof of the model has been presented, using Universal Composibility (UC) framework.

At present, secure multi-party computing is an effective solution for organizations and institutions that want to derive greater value and benefit from the collaborative computing of their data. Most current secure multi-party computing solutions use encryption schemes that are not resistant to quantum attacks, which is a security risk in today’s quickly growing quantum computing, and, when obtaining results, the result querier needs to collect the private keys of multiple data owners to jointly decrypt them, or there needs to be an interaction between the data owner and the querier during the decryption process. Based on the NTRU cryptosystem, which is resistant to quantum computing attacks and has a simple and easy-to-implement structure, and combined with multi-key fully homomorphic encryption (MKFHE) and proxy re-encryption, this paper proposes a secure multi-party computing scheme based on NTRU-type multi-key fully homomorphic proxy re-encryption in the blockchain environment, using the blockchain as trusted storage and a trusted execution environment to provide data security for multi-party computing. The scheme meets the requirements of being verifiable, conspiracy-proof, individually decryptable by the querier, and resistant to quantum attacks.

A continuous variable (CV) controlled quantum dialogue (QD) scheme is proposed. The scheme is further modified to obtain two other protocols of (CV) secure multiparty computation. The first one of these protocols provides a solution of two-party socialist millionaire problem, while the second protocol provides a solution for a special type of multi-party socialist millionaire problem which can be viewed as a protocol for multiparty quantum private comparison. It is shown that the proposed scheme of (CV) controlled (QD) can be performed using bipartite entanglement and can be reduced to obtain several other two- and three-party cryptographic schemes in the limiting cases. The security of the proposed scheme and its advantage over corresponding discrete variable (DV) counterpart are also discussed. Specifically, the ignorance of an eavesdropper, i.e., information encoded by Alice/Bob, in the proposed scheme is shown to be more than that in the corresponding (DV) scheme, and thus the present scheme is less prone to information leakage inherent with the (DV) (QD) based schemes. It is further established that the proposed scheme can be viewed as a (CV) counterpart of quantum cryptographic switch which allows a supervisor to control the information transferred between the two legitimate parties to a continuously varying degree.

Randomized encoding is a powerful cryptographic primitive with various applications such as secure multiparty computation, verifiable computation, parallel cryptography, and complexity lower bounds. Intuitively, randomized encoding $\hat{f}$ of a function $f$ is another function such that $f(x)$ can be recovered from $\hat{f}(x)$, and nothing except for $f(x)$ is leaked from $\hat{f}(x)$. Its quantum version, quantum randomized encoding, has been introduced recently [Brakerski and Yuen, arXiv:2006.01085]. Intuitively, quantum randomized encoding $\hat{F}$ of a quantum operation $F$ is another quantum operation such that, for any quantum state $\rho$, $F(\rho)$ can be recovered from $\hat{F}(\rho)$, and nothing except for $F(\rho)$ is leaked from $\hat{F}(\rho)$. In this paper, we show three results. First, we show that if quantum randomized encoding of BB84 state generations is possible with an encoding operation $E$, then a two-round verification of quantum computing is possible with a classical verifier who can additionally do the operation $E$. One of the most important goals in the field of the verification of quantum computing is to construct a verification protocol with a verifier as classical as possible. This result therefore demonstrates a potential application of quantum randomized encoding to the verification of quantum computing: if we can find a good quantum randomized encoding (in terms of the encoding complexity), then we can construct a good verification protocol of quantum computing. Our second result is, however, to show that too good quantum randomized encoding is impossible: if quantum randomized encoding for the generation of even simple states (such as BB84 states) is possible with a classical encoding operation, then the no-cloning is violated. Finally, we consider a natural modification of blind quantum computing protocols in such a way that the server gets the output like quantum randomized encoding. We show that the modified protocol is not secure.

Security and Privacy of Blockchain and Quantum Computation

The cryptographic task of secure multi-party (classical) computation has received a lot of attention in the last decades. Even in the extreme case where a computation is performed between $k$ mutually distrustful players, and security is required even for the single honest player if all other players are colluding adversaries, secure protocols are known. For quantum computation, on the other hand, protocols allowing arbitrary dishonest majority have only been proven for $k=2$. In this work, we generalize the approach taken by Dupuis, Nielsen and Salvail (CRYPTO 2012) in the two-party setting to devise a secure, efficient protocol for multi-party quantum computation for any number of players $k$, and prove security against up to $k-1$ colluding adversaries. The quantum round complexity of the protocol for computing a quantum circuit of $\{\mathsf{CNOT, T}\}$ depth $d$ is $O(k \cdot (d + \log n))$, where $n$ is the security parameter. To achieve efficiency, we develop a novel public verification protocol for the Clifford authentication code, and a testing protocol for magic-state inputs, both using classical multi-party computation.

Purpose: To explore the vast potential and possibilities that arise from synergizing quantum computing with other foundational technologies in the field of Information, Communication, and Computing Technologies (ICCT). By integrating quantum computing with other ICCT technologies, such as artificial intelligence, data analytics, cryptography, and communication networks, researchers aim to unlock unprecedented computational power and efficiency, thereby revolutionizing various industries and scientific domains. This research seeks to unravel novel applications, enhance the robustness and scalability of quantum computing systems, and pave the way for transformative advancements that will shape the future of information processing and communication paradigms. Ultimately, this interdisciplinary exploration holds the key to unleashing the full capabilities of quantum computing and opens doors to groundbreaking innovations that were once considered beyond reach. Methodology: Exploratory research method is used to analyse and interpret various related information collected using secondary sources using Google search engine and Google Scholar search engine as well as using quasi-secondary sources including AI engine supported GPT and Bard. ABCD analysis framework is used to study the advantages, benefits, constraints, and disadvantages of integration of Quantum computing technology with other ICCT Underlying Technologies. Finally, the results are interpreted and concluded by developing 12 postulates. Findings: The results demonstrate the potential of integrating quantum computing with other ICCT underlying technologies, offering transformative improvements in computational power, security, and efficiency across various industries and applications. As quantum computing continues to advance, its integration with other ICCT technologies will lead to new opportunities for innovation and the development of more sophisticated and powerful information and communication systems. Originality/Value: The paper evaluates advances and new research opportunities in the area of quantum computing technology. A new idea of integration of quantum computing technology with other ICCT underlying technologies is proposed and the advantages, benefits, constraints, and disadvantages of integration of Quantum computing technology with other ICCT Underlying Technologies are analysed using the ABCD analysis framework. The results are interpreted in the form of 12 new postulates. Type of Paper: Exploratory research

Recent advances in quantum technology and the potential that practical quantum computers may become a reality in the future have led to renewed interest in developing cryptographic technologies that are secure against conventional and quantum attacks. Currently, virtually all asymmetric cryptographic schemes in use are threatened by the potential development of powerful quantum computers. Post-quantum cryptography is one of main the ways to combat this threat. Its security is based on the complexity of mathematical problems that are currently considered unsolvable efficiently, even with the help of quantum computers. The security of information systems is ensured through protection against various threats that use system vulnerabilities. Security protocols are the building blocks of secure communication. They implement security mechanisms to provide security services. Security protocols are considered abstract when analyzed, but may have additional vulnerabilities in implementation. This work contains a holistic study of security protocols. Basics of security protocols, taxonomy of attacks on security protocols and their implementation are considered, as well as various methods and models of protocol security analysis. In particular, the differences between information-theoretic and computational security, computational and symbolic models are specified. In addition, an overview of the computational security models for Authenticated Key Exchange (AKE) and Password Authentication Key Exchange (PAKE) protocols is provided. The most important security models for the AKE and PAKE protocols were also described. With the emergence of new technologies that may have different security requirements, as well as with increased opportunities for competition, there is always a need to develop new protocols. Thus, the purpose of this article is to review, classify, analyze, and research the vulnerabilities of information systems from classical, quantum, and special attacks, performed taking into account the forecast regarding the possibilities of attacks on post-quantum cryptographic transformations; studying security assessment models for existing cryptographic protocols, as well as reviewing and benchmarking security models and providing suggestions for protection against existing potential attacks.

With a recent increase in the advancement of the technology, computer system and it’s sensitive data are getting exhibited to unauthorised users, with steadily corroding the fundamentals of computer security. This, in fact, demanded fundamental innovations that require several cryptographic paradigms and security protocol. Previously it was thought that asymmetric cryptographic key like RSA, Diffie-Hellman, are very hard to decrypt by classical computation, but with the implementation of quantum computation, it is proved that this kind of cryptographic algorithm is very easy to decrypt and hackers can steal important Data [3], [6]. After the release of Ajtai’s research paper Generating Hard Instances of Lattice Problem [2], a million reasons arose in the minds of the researchers to develop on methods based on lattice-based cryptography to improve their security needs. Firstly, it provided much stronger security, and the average-case of several problems in lattice-based cryptography seems equivalent to the worst-case problem of these problems [2]. Moreover, lattices have the potential to persuade the cryptanalytic attacks created by any quantum computers i.e Quantum Computational Secure [3]. In this paper, we will discuss lattice-based cryptosystem, it’s security dimensions, a general brief on how it works, future scope, applications and areas of Interest.