Abstract Lattice-based commitment schemes and their associated zero-knowledge proofs are essential building blocks for advanced lattice-based cryptographic protocols. In particular, proofs of algebraic relations among committed messages are widely used in privacy-preserving protocols such as range proofs. At CRYPTO 2020, Attema et al. proposed practical proofs for valid openings and multiplicative relations among committed values using the BDLOP commitment scheme. In their work, all commitments are generated using the same short randomness. In this paper, we consider a batch setting where commitments are generated using $$\ell$$ ℓ independent random vectors and present a batch valid opening proof. Our construction generalizes the approach of Baum et al. by supporting a larger challenge set and removing the requirement for invertible challenge differences. As a result, the proof size scales logarithmically with $$\ell$$ ℓ , rather than linearly. Furthermore, we introduce a product proof for committed messages with shared randomness across these $$\ell$$ ℓ commitment groups. Compared to the naive approach of applying Attema’s product proof once and repeating the opening proof $$\ell -1$$ ℓ - 1 times, our method achieves significantly better communication efficiency.

Volumetric video technology enables an immersive and interactive experience for viewers. However, the point cloud frames composing the video are prohibitively large, imposing significant demands on storage and transmission. The existing inter-frame compression methods perform motion estimation based on a fixed block size, ignoring the inherent motion characteristics of the volumetric content and failing to fully utilize the inter-frame redundancy information. To overcome this limitation, we introduce MP-VVC, a patch-adaptive volumetric video compression framework based on volumetric content and motion analysis. Specifically, MP-VVC involves a two-stage segmentation of volumetric frames, body-part segmentation and patch segmentation. Subsequently, we employ the iterative closest point algorithm to determine rigid movements of body parts and patches. Furthermore, we also take into account the volumetric content of non-rigid movements and compress it. Experiments show that MP-VVC reduces the bitrate by up to$7.58 \times$compared with two baselines, with at most$3.63 \times$improvement in decoding time compared with MP3DG.

The BDLOP commitment of Baum et al. (SCN 2018) is the currently most efficient commitment scheme. Based on BDLOP commitment, Attema et al. in CRYPTO 2020 presented an efficient product proof in the ring Rq = ℤq[X]/(Xd + 1) where Xd + 1 splits into low-degree factors (ALS scheme). Their proof has only one garbage commitment besides the necessary opening proof and works in the case that all the messages are committed simultaneously using the same randomness r⃗. In this paper, we deal with the case where the messages involved in the multiplicative relation are committed using different randomnesses, and construct a parallel product proof and two sequential product proofs. Both of which still require need one additional garbage commitment.

Machine learning has been a driving force in the evolution of tremendous computing services and applications in the past decade. Traditional learning systems rely on centralized training and inference, which poses serious privacy and security concerns. To solve this problem, distributed learning over wireless edge networks (DLWENs) emerges as a trending solution and has attracted increasing research interests. In DLWENs, corresponding services need to be placed onto the edge servers to process the distributed tasks. Apparently, different placement of training services can significantly affect the performance of all distributed learning tasks. In this article, we propose TASP, a task-aware service placement scheme for distributed learning in wireless edge networks. By carefully considering the structures (directed acyclic graphs) of the distributed learning tasks, the fine-grained task requests and inter-task dependencies are incorporated into the placement strategies to realize the parallel computation of learning services. We also exploit queuing theory to characterize the dynamics caused by task uncertainties. Extensive experiments based on the Alibaba ML dataset show that, compared to the state-of-the-art schemes, the proposed work reduces the overall delay of distributed learning tasks by 38.6% on average.

Abstract Bootle et al. in CRYPTO 2019 proposed a zero knowledge proof for an $\mathrm{ISIS}_{m,n,q,\beta }$ instance $A\vec{s} = \vec{u} \bmod q$ with $\|\vec{s}\|_{\infty }\leq \beta $ (BLS scheme). It was implemented by transforming the instance into the form $A^{\prime }\vec{s}^{\prime } =\vec{u}\bmod q$, where the coefficients of $\vec{s}^{\prime}$ are in $\{0,1,2\}$, and proved the latter in an exact way. With the concrete parameters $m=1024,n=2048,\beta =1,q\approx 2^{32}$, their proof is of length 384.03KB. In this paper, we decrease the proof size of BLS scheme by two techniques. The first one takes effect on some special parameters. For these parameters, using the binary basic set instead of the ternary one results in a shorter proof. The second one deals with the repetition of the lower half in BLS scheme. Observing that what the lower half proves is of form $\mathbf{B}\vec{\mathbf{r}}=\vec{\mathbf{t}}$ with a short vector $\vec{\mathbf{r}}$ of polynomials, a variant of parallel repetition can be used to shorten the proof size. Combining these two techniques together, the proof size of the above-mentioned instance can be reduced to 220.01KB, only 57.3$\%$ of BLS scheme.

3D segmentation of nuclei images is a fundamental task for many biological studies. Despite the rapid advances of large-volume 3D imaging acquisition methods and the emergence of sophisticated algorithms to segment the nuclei in recent years, a benchmark with all cells completely annotated is still missing, making it hard to accurately assess and further improve the performance of the algorithms. The existing nuclei segmentation benchmarks either worked on 2D only or annotated a small number of 3D cells, perhaps due to the high cost of 3D annotation for large-scale data. To fulfill the critical need, we constructed NIS3D, a 3D, high cell density, large-volume, and completely annotated Nuclei Image Segmentation benchmark, assisted by our newly designed semi-automatic annotation software. NIS3D provides more than 22,000 cells across multiple most-used species in this area. Each cell is labeled by three independent annotators, so we can measure the variability of each annotation. A confidence score is computed for each cell, allowing more nuanced testing and performance comparison. A comprehensive review on the methods of segmenting 3D dense nuclei was conducted. The benchmark was used to evaluate the performance of several selected state-of-the-art segmentation algorithms. The best of current methods is still far away from human-level accuracy, corroborating the necessity of generating such a benchmark. The testing results also demonstrated the strength and weakness of each method and pointed out the directions of further methodological development. The dataset can be downloaded here: https://github.com/yu-lab-vt/NIS3D.

Multiple time series data occur in many real applications and the alignment among them is usually a fundamental step of data analysis. Frequently, these multiple time series are inter-dependent, which provides extra information for the alignment task and this information cannot be well utilized in the conventional pairwise alignment methods. Recently, the joint alignment was modeled as a max-flow problem, in which both the profile similarity between the aligned time series and the distance between adjacent warping functions are jointly optimized. However, despite the new model having elegant mathematical formulation and superior alignment accuracy, the long computation time and large memory usage, due to the use of the existing general-purpose max-flow algorithms, limit significantly its well-deserved wide use. In this report, we present BIdirectional pushing with Linear Component Operations (BILCO), a novel algorithm that solves the joint alignment max-flow problems efficiently and exactly. We develop the strategy of linear component operations that integrates dynamic programming technique and the push-relabel approach. This strategy is motivated by the fact that the joint alignment max-flow problem is a generalization of dynamic time warping (DTW) and numerous individual DTW problems are embedded. Further, a bidirectional-pushing strategy is proposed to introduce prior knowledge and reduce unnecessary computation, by leveraging another fact that good initialization can be easily computed for the joint alignment max-flow problem. We demonstrate the efficiency of BILCO using both synthetic and real experiments. Tested on thousands of datasets under various simulated scenarios and in three distinct application categories, BILCO consistently achieves at least 10 and averagely 20-folds increase in speed, and uses at most 1/8 and averagely 1/10 memory compared with the best existing max-flow method. Our source code can be found at https://github.com/yu-lab-vt/BILCO.

Zero-knowledge proof for any relation amongst committed values is crucial and widely applicable in the design of high level cryptographic schemes, especially in privacy-preserving protocols. Besides quantum resistance, efficiency is what we are most concerned about, including asymptotic efficiency and concrete efficiency. Jain et al. proposed a simple string commitment scheme based on the Learning Parity with Noise (LPN) problem (JKPT12), and then designed zero-knowledge proofs for valid opening, linear relation and multiplicative relation of committed values. As a result, they got an efficient zero-knowledge proof for any circuit C, with communication complexity $\mathcal{O}(t|C|\ell \log \ell )$, where t is a security parameter measuring soundness and ℓ is the secret length of the LPN problem. In this work, we improve the concrete communication complexity by combining some commitments in JKPT12 together. The proofs of linear relation and multiplicative relation are shortened by (6α + 4)ℓ and (42α+28)ℓ respectively, where ℓ is the size of LPN secret. As a result, the communication cost of the protocol proving arbitrary relation is reduced by a constant level.

The variance-stabilizing transformation (VST) problem is to transform heteroscedastic data to homoscedastic data so that they are more tractable for subsequent analysis. However, most of the existing approaches focus on finding an analytical solution for a certain parametric distribution, which severely limits the applications, because simple distributions cannot faithfully describe the real data while more complicated distributions cannot be analytically solved. In this paper, we converted the VST problem into a convex optimization problem, which can always be efficiently solved, identified the specific structure of the convex problem, which further improved the efficiency of the proposed algorithm, and showed that any finite discrete distributions and the discretized version of any continuous distributions from real data can be variance-stabilized in an easy and nonparametric way. We demonstrated the new approach on bioimaging data and achieved superior performance compared to peer algorithms in terms of not only the variance homoscedasticity but also the impact on subsequent analysis such as denoising. Source codes are available at https://github.com/yu-lab-vt/ConvexVST.

On the ground with complex electromagnetic environment, protecting the received satellite signals from interference is a key issue for the receiver. To effectively cope with the coexistence of jamming and spoofing interference, and accurately suppress both jamming and spoofing, in this paper, a hybrid interference suppression algorithm based on robust beamforming is proposed. The combination of subspace projection algorithm, despreading algorithm, multiple signal classification (MUSIC) algorithm and robust beamforming based on the linearly constrained minimum variance criterion can suppress both jamming and spoofing effectively, and ensure that the desired signal is undistorted. At the same time, it can overcome the problem of inaccurate direction estimation under low signal-to-noise ratio. Numerical examples show that the proposed algorithm can effectively suppress hybrid interference.