Post Quantum Cryptography: Techniques, Challenges, Standardization, and Directions for Future Research

Bavdekar, Ritik; Chopde, Eashan Jayant; Bhatia, Ashutosh; Tiwari, Kamlesh; Daniel, Sandeep Joshua; Atul, Atul

doi:10.48550/arxiv.2202.02826

Cited by 3 publications

(4 citation statements)

References 24 publications

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“…Comparably, the Raspberry Pi 4 has similar specifications and therefore more closely represents the IoT devices of today. Previous studies have either used non-constrained devices [40,42] due to the availability of technology or devices of very limited capability compared to current IoT device standards [58][59][60]. Furthermore, some finalist PQAs, such as BIKE and Classic McEliece, have been modified for performance optimization between the second and third rounds of the NIST competition, and it is therefore prudent to test the most recent iterations of these PQAs [24,25].…”

Section: Discussionmentioning

confidence: 99%

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Constrained Device Performance Benchmarking with the Implementation of Post-Quantum Cryptography

Fitzgibbon,

Ottaviani

2024

Cryptography

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Advances in quantum computers may pose a significant threat to existing public-key encryption methods, which are crucial to the current infrastructure of cyber security. Both RSA and ECDSA, the two most widely used security algorithms today, may be (in principle) solved by the Shor algorithm in polynomial time due to its ability to efficiently solve the discrete logarithm problem, potentially making present infrastructures insecure against a quantum attack. The National Institute of Standards and Technology (NIST) reacted with the post-quantum cryptography (PQC) standardization process to develop and optimize a series of post-quantum algorithms (PQAs) based on difficult mathematical problems that are not susceptible to being solved by Shor’s algorithm. Whilst high-powered computers can run these PQAs efficiently, further work is needed to investigate and benchmark the performance of these algorithms on lower-powered (constrained) devices and the ease with which they may be integrated into existing protocols such as TLS. This paper provides quantitative benchmark and handshake performance data for the most recently selected PQAs from NIST, tested on a Raspberry Pi 4 device to simulate today’s IoT (Internet of Things) devices, and provides quantitative comparisons with previous benchmarking data on a range of constrained systems. CRYSTALS-Kyber and CRYSTALS-Dilithium are shown to be the most efficient PQAs in the key encapsulation and signature algorithms, respectively, with Falcon providing the optimal TLS handshake size.

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Section: Discussionmentioning

confidence: 99%

“…Given the increase in the number of IoT devices transmitting encrypted data, the work developed in ref. [40] discusses the need for 'lightweight' PQAs that are quantumsafe but with minimal overheads. Ref.…”

Section: Post-quantum Cryptography and Iot Devicesmentioning

confidence: 99%

Constrained Device Performance Benchmarking with the Implementation of Post-Quantum Cryptography

Fitzgibbon,

Ottaviani

2024

Cryptography

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“…These include optimisation problems, cryptography, and simulation of quantum systems. For example, quantum computers have the potential to break classical cryptographic protocols, such as RSA encryption, by factoring large numbers exponentially faster than classical algorithms (Bavdekar, Chopde, Bhatia, Tiwari, & Daniel, 2022;Petrenko & Petrenko, 2022). Furthermore, quantum computing offers the promise of more efficient algorithms for a wide range of applications, from drug discovery and materials science to financial modeling and machine learning.…”

Section: Advantages Of Quantum Computingmentioning

confidence: 99%

Quantum computing and financial risk management: A theoretical review and implications

Mayokun Daniel Adegbola,

Ayodeji Enoch Adegbola,

Prisca Amajuoyi

et al. 2024

Comput. sci. IT res. j.

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This review paper examines the potential implications of quantum computing for financial risk management. It explores the fundamental principles of quantum computing, including qubits, superposition, and entanglement. It discusses its advantages over classical computing for risk assessment and mitigation. The paper outlines traditional approaches to financial risk management. It explores how quantum algorithms, such as quantum Monte Carlo methods and quantum annealing, can enhance these strategies. Challenges and barriers to adopting quantum computing in the financial industry are identified, along with future research directions. Ultimately, the paper highlights the transformative potential of quantum computing for improving risk management in today's complex financial markets. Keywords: Quantum Computing, Financial Risk Management, Qubits, Quantum Algorithms, Monte Carlo Simulations, Portfolio Optimisation.

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“…Data flowing through encrypted networks has become harder to exploit using existing means. Reference [13] proposes a cryptography technique that is impenetrable even by brute force with a quantum computer. However, with the advent of machine learning and deep learning algorithms, researchers have started taking a different approach for deciphering network data.…”

Section: Related Workmentioning

confidence: 99%

Explainable YouTube Video Identification Using Sufficient Input Subsets

et al. 2023

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Neural network models are black boxes in nature. The mechanics behind these black boxes are practically unexplainable. Having the insight into patterns identified by these algorithms can help unravel important properties of the subject in query. These artificial intelligence based algorithms are used in every domain for prediction. This research focuses on patterns formed in network traffic that can be leveraged to identify videos streaming over the network. The proposed work uses a sufficient input subset (SIS) model on two separate video identification techniques to understand and explain the patterns detected by the techniques. The first technique creates the fingerprints of videos on a period-based algorithm to handle variable bitrate inconsistencies. These fingerprints are passed to a convolutional Neural Network (CNN) for pattern recognition. The second technique is based on traffic pattern plot identification that creates a graph of packet size with respect to time for each stream before passing that to a CNN as an image. For model explainability, a sufficient input subset (SIS) model is used to identify features that are sufficient to reach the same prediction under a certain threshold of confidence by the model. The generated SIS of each input sample is clustered using DBSCAN, K-Means, and cosine-based Hierarchical clustering. The clustered SIS highlight the common patterns for each class. The SIS patterns learnt by each model of three individual videos are discussed. Furthermore, these patterns are used to investigate misclassification and provide a rationale behind it to justify the working of the classifier model.

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Post Quantum Cryptography: Techniques, Challenges, Standardization, and Directions for Future Research

Cited by 3 publications

References 24 publications

Constrained Device Performance Benchmarking with the Implementation of Post-Quantum Cryptography

Constrained Device Performance Benchmarking with the Implementation of Post-Quantum Cryptography

Quantum computing and financial risk management: A theoretical review and implications

Explainable YouTube Video Identification Using Sufficient Input Subsets

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