On the Entropy of Oscillator-Based True Random Number Generators

True random number generators (TRNGs) are essential for cryptographic systems, and they are usually evaluated by the concept of entropy. In general, the entropy of a TRNG is estimated from its stochastic model, and reflected in the statistical results of the generated raw bits. Oscillator-based TRNGs are widely used in practical cryptographic systems due to its elegant structure, and its stochastic model has been studied in different aspects. In this paper, we investigate the applicability of the different entropy estimation methods for oscillator-based TRNGs, including the bit-rate entropy, the lower bound and the approx imate entropy. Particularly, we firstly analyze the two existing stochastic models (one of which is phase-based and the other is time-based), and deduce consistent bit-rate entropy results from these two models. Then, we design an approximate entropy calculation method on the output raw bits of a simulated oscillator-based TRNG, and this statistical calculation result well matches the bit-rate entropy from stochastic models. In addition, we discuss the extreme case of tiny randomness where some methods are inapplicable, and provide the recommendations for these entropy evaluation methods. Finally, we design a hardware verification method in a real oscillator-based TRNG, and validate these estimation methods in the hardware platform

Utilizing the Double-Precision Floating-Point Computing Power of GPUs for RSA Acceleration

Asymmetric cryptographic algorithm (e.g., RSA and Elliptic Curve Cryptography) implementations on Graphics Processing Units (GPUs) have been researched for over a decade. The basic idea of most previous contributions is exploiting the highly parallel GPU architecture and porting the integer-based algorithms from general-purpose CPUs to GPUs, to offer high performance. However, the great potential cryptographic computing power of GPUs, especially by the more powerful floating-point instructions, has not been comprehensively investigated in fact. In this paper, we fully exploit the floating-point computing power of GPUs, by various designs, including the floating-point-based Montgomery multiplication/exponentiation algorithm and Chinese Remainder Theorem (CRT) implementation in GPU. And for practical usage of the proposed algorithm, a new method is performed to convert the input/output between octet strings and floating-point numbers, fully utilizing GPUs and further promoting the overall performance by about 5%. The performance of RSA-2048/3072/4096 decryption on NVIDIA GeForce GTX TITAN reaches 42,211/12,151/5,790 operations per second, respectively, which achieves 13 times the performance of the previous fastest floating-point-based implementation (published in Eurocrypt 2009). The RSA-4096 decryption precedes the existing fastest integer-based result by 23%

UPPRESSO: Untraceable and Unlinkable Privacy-PREserving Single Sign-On Services

Single sign-on (SSO) allows a user to maintain only the credential at the identity provider (IdP), to login to numerous RPs. However, SSO introduces extra privacy threats, compared with traditional authentication mechanisms, as (a) the IdP could track all RPs which a user is visiting, and (b) collusive RPs could learn a user's online profile by linking his identities across these RPs. This paper proposes a privacypreserving SSO system, called UPPRESSO, to protect a user's login activities against both the curious IdP and collusive RPs. We analyze the identity dilemma between the security requirements and these privacy concerns, and convert the SSO privacy problems into an identity transformation challenge. In each login instance, an ephemeral pseudo-identity (denoted as PID_RP ) of the RP, is firstly negotiated between the user and the RP. PID_RP is sent to the IdP and designated in the identity token, so the IdP is not aware of the visited RP. Meanwhile, PID_RP is used by the IdP to transform the permanent user identity ID_U into an ephemeral user pseudo-identity (denoted as PID_U ) in the identity token. On receiving the identity token, the RP transforms PID_U into a permanent account (denoted as Acct) of the user, by an ephemeral trapdoor in the negotiation. Given a user, the account at each RP is unique and different from ID_U, so collusive RPs cannot link his identities across these RPs. We build the UPPRESSO prototype on top of MITREid Connect, an open-source implementation of OIDC. The extensive evaluation shows that UPPRESSO fulfills the requirements of both security and privacy and introduces reasonable overheads

ConvKyber: Unleashing the Power of AI Accelerators for Faster Kyber with Novel Iteration-based Approaches

The remarkable performance capabilities of AI accelerators offer promising opportunities for accelerating cryptographic algorithms, particularly in the context of lattice-based cryptography. However, current approaches to leveraging AI accelerators often remain at a rudimentary level of implementation, overlooking the intricate internal mechanisms of these devices. Consequently, a significant number of computational resources is underutilized. In this paper, we present a comprehensive exploration of NVIDIA Tensor Cores and introduce a novel framework tailored specifically for Kyber. Firstly, we propose two innovative approaches that efficiently break down Kyber\u27s NTT into iterative matrix multiplications, resulting in approximately a 75% reduction in costs compared to the state-of-the-art scanning-based methods.Secondly, by reversing the internal mechanisms, we precisely manipulate the internal resources of Tensor Cores using assembly-level code instead of inefficient standard interfaces, eliminating memory accesses and redundant function calls. Finally, building upon our highly optimized NTT, we provide a complete implementation for all parameter sets of Kyber. Our implementation surpasses the state-of-the-art Tensor Core based work, achieving remarkable speed-ups of 1.93x, 1.65x, 1.22x and 3.55x for polyvec_ntt, KeyGen, Enc and Dec in Kyber-1024, respectively. Even when considering execution latency, our throughput-oriented full Kyber implementation maintains an acceptable execution latency. For instance, the execution latency ranges from 1.02 to 5.68 milliseconds for Kyber-1024 on R3080 when achieving the peak throughput

ConvKyber: Unleashing the Power of AI Accelerators for Faster Kyber with Novel Iteration-based Approaches

The remarkable performance capabilities of AI accelerators offer promising opportunities for accelerating cryptographic algorithms, particularly in the context of lattice-based cryptography. However, current approaches to leveraging AI accelerators often remain at a rudimentary level of implementation, overlooking the intricate internal mechanisms of these devices. Consequently, a significant number of computational resources is underutilized. In this paper, we present a comprehensive exploration of NVIDIA Tensor Cores and introduce a novel framework tailored specifically for Kyber. Firstly, we propose two innovative approaches that efficiently break down Kyber’s NTT into iterative matrix multiplications, resulting in approximately a 75% reduction in costs compared to the state-of-the-art scanning-based methods. Secondly, by reversing the internal mechanisms, we precisely manipulate the internal resources of Tensor Cores using assembly-level code instead of inefficient standard interfaces, eliminating memory accesses and redundant function calls. Finally, building upon our highly optimized NTT, we provide a complete implementation for all parameter sets of Kyber. Our implementation surpasses the state-of-the-art Tensor Core based work, achieving remarkable speed-ups of 1.93x, 1.65x, 1.22x and 3.55x for polyvec_ntt, KeyGen, Enc and Dec in Kyber-1024, respectively. Even when considering execution latency, our throughput-oriented full Kyber implementation maintains an acceptable execution latency. For instance, the execution latency ranges from 1.02 to 5.68 milliseconds for Kyber-1024 on R3080 when achieving the peak throughput

A Novel High-performance Implementation of CRYSTALS-Kyber with AI Accelerator

Public-key cryptography, including conventional cryptosystems and post-quantum cryptography, involves computation-intensive workloads. With noticing the extraordinary computing power of AI accelerators, in this paper, we further explore the feasibility to introduce AI accelerators into high-performance cryptographic computing. Since AI accelerators are dedicated to machine learning or neural networks, the biggest challenge is how to transform cryptographic workloads into their operations, while ensuring the correctness of the results and bringing convincing performance gains. After investigating and analysing the workload of NVIDIA AI accelerator, Tensor Core, we choose to utilize it to accelerate the polynomial multiplication, usually the most time-consuming part in lattice-based cryptography. We take measures to accommodate the matrix-multiply-and-add mode of Tensor Core and make a trade-off between precision and performance, to leverage it as a high-performance NTT box performing NTT/INTT through CUDA C++ WMMA APIs. Meanwhile, we take CRYSTALS-Kyber, the candidate to be standardized by NIST, as a case study on RTX 3080 with the Ampere Tensor Core. The empirical results show that the customized NTT of polynomial vector ($n=256,k=4$) with our NTT box obtains a speedup around 6.47x that of the state-of-the-art implementation on the same GPU platform. Compared with the AVX2 implementation submitted to NIST, our Kyber-1024 can achieve a speedup of 26x, 36x, and 35x for each phase

The Epitope Study on the SARS-CoV Nucleocapsid Protein

The nucleocapsid protein (N protein) has been found to be an antigenic protein in a number of coronaviruses. Whether the N protein in severe acute respiratory syndrome-associated coronavirus (SARS-CoV) is antigenic remains to be elucidated. Using Western blot and Enzyme-linked Immunosorbent Assay (ELISA), the recombinant N proteins and the synthesized peptides derived from the N protein were screened in sera from SARS patients. All patient sera in this study displayed strong positive immunoreactivities against the recombinant N proteins, whereas normal sera gave negative immunoresponses to these proteins, indicating that the N protein of SARS-CoV is an antigenic protein. Furthermore, the epitope sites in the N protein were determined by competition experiments, in which the recombinant proteins or the synthesized peptides competed against the SARS-CoV proteins to bind to the antibodies raised in SARS sera. One epitope site located at the C-terminus was confirmed as the most antigenic region in this protein. A detailed screening of peptide with ELISA demonstrated that the amino sequence from Codons 371 to 407 was the epitope site at the C-terminus of the N protein. Understanding of the epitope sites could be very significant for developing an effective diagnostic approach to SARS

The Genomes of Oryza sativa: A History of Duplications

We report improved whole-genome shotgun sequences for the genomes of indica and japonica rice, both with multimegabase contiguity, or almost 1,000-fold improvement over the drafts of 2002. Tested against a nonredundant collection of 19,079 full-length cDNAs, 97.7% of the genes are aligned, without fragmentation, to the mapped super-scaffolds of one or the other genome. We introduce a gene identification procedure for plants that does not rely on similarity to known genes to remove erroneous predictions resulting from transposable elements. Using the available EST data to adjust for residual errors in the predictions, the estimated gene count is at least 38,000–40,000. Only 2%–3% of the genes are unique to any one subspecies, comparable to the amount of sequence that might still be missing. Despite this lack of variation in gene content, there is enormous variation in the intergenic regions. At least a quarter of the two sequences could not be aligned, and where they could be aligned, single nucleotide polymorphism (SNP) rates varied from as little as 3.0 SNP/kb in the coding regions to 27.6 SNP/kb in the transposable elements. A more inclusive new approach for analyzing duplication history is introduced here. It reveals an ancient whole-genome duplication, a recent segmental duplication on Chromosomes 11 and 12, and massive ongoing individual gene duplications. We find 18 distinct pairs of duplicated segments that cover 65.7% of the genome; 17 of these pairs date back to a common time before the divergence of the grasses. More important, ongoing individual gene duplications provide a never-ending source of raw material for gene genesis and are major contributors to the differences between members of the grass family

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead