Incorporating Zero-Knowledge Succinct Non-interactive Argument of Knowledge for Blockchain-based Identity Management with off-chain computations

In today's world, secure and efficient biometric authentication is of keen importance. Traditional authentication methods are no longer considered reliable due to their susceptibility to cyber-attacks. Biometric authentication, particularly fingerprint authentication, has emerged as a promising alternative, but it raises concerns about the storage and use of biometric data, as well as centralized storage, which could make it vulnerable to cyber-attacks. In this paper, a novel blockchain-based fingerprint authentication system is proposed that integrates zk-SNARKs, which are zero-knowledge proofs that enable secure and efficient authentication without revealing sensitive biometric information. A KNN-based approach on the FVC2002, FVC2004 and FVC2006 datasets is used to generate a cancelable template for secure, faster, and robust biometric registration and authentication which is stored using the Interplanetary File System. The proposed approach provides an average accuracy of 99.01%, 98.97% and 98.52% over the FVC2002, FVC2004 and FVC2006 datasets respectively for fingerprint authentication. Incorporation of zk-SNARK facilitates smaller proof size. Overall, the proposed method has the potential to provide a secure and efficient solution for blockchain-based identity management

Privately Estimating a Gaussian: Efficient, Robust and Optimal

In this work, we give efficient algorithms for privately estimating a Gaussian distribution in both pure and approximate differential privacy (DP) models with optimal dependence on the dimension in the sample complexity. In the pure DP setting, we give an efficient algorithm that estimates an unknown $d$-dimensional Gaussian distribution up to an arbitrary tiny total variation error using $\widetilde{O}(d^2 \log \kappa)$ samples while tolerating a constant fraction of adversarial outliers. Here, $\kappa$ is the condition number of the target covariance matrix. The sample bound matches best non-private estimators in the dependence on the dimension (up to a polylogarithmic factor). We prove a new lower bound on differentially private covariance estimation to show that the dependence on the condition number $\kappa$ in the above sample bound is also tight. Prior to our work, only identifiability results (yielding inefficient super-polynomial time algorithms) were known for the problem. In the approximate DP setting, we give an efficient algorithm to estimate an unknown Gaussian distribution up to an arbitrarily tiny total variation error using $\widetilde{O}(d^2)$ samples while tolerating a constant fraction of adversarial outliers. Prior to our work, all efficient approximate DP algorithms incurred a super-quadratic sample cost or were not outlier-robust. For the special case of mean estimation, our algorithm achieves the optimal sample complexity of $\widetilde O(d)$, improving on a $\widetilde O(d^{1.5})$ bound from prior work. Our pure DP algorithm relies on a recursive private preconditioning subroutine that utilizes the recent work on private mean estimation [Hopkins et al., 2022]. Our approximate DP algorithms are based on a substantial upgrade of the method of stabilizing convex relaxations introduced in [Kothari et al., 2022]

Unveiling the Significance of Body Mass Index in Diagnosis of Superior Mesenteric Artery Syndrome: A Hidden Link

Background & Aims: The superior mesenteric artery syndrome (SMAS) is an uncommon syndrome characterized by the compression of the third part of the duodenum between the superior mesenteric artery (SMA) and abdominal aorta with resultant proximal duodenal dilatation. The radiological diagnosis of the SMAS is based on reduced angle and distance between the SMA and aorta in presence of proximal duodenal dilatation. A reduction in these is closely associated with depletion of the mesenteric fat between the vessels. Our primary aim is to establish the relationship, if any, of body mass index (BMI) with the angle and distance between the SMA and abdominal aorta in general population. Materials and Methods: This study was carried out in 200 patients who had undergone contrast enhanced computed tomography for various other complaints. Various parameters such as aortomesenteric distance (AMD) and aortomesenteric angle (AMA) along with the body mass indices were calculated. Pearson correlation coefficients were calculated to establish the relationship between BMI, AMD and AMA. Results: Pearson‘s correlation coefficient for BMI and AMD was 0.868, indicating strong positive correlation and 0.577 for BMI and AMA, indicating moderate positive correlation. Furthermore, AMD and AMA also showed positive correlation with Pearson‘s correlation coefficient of 0.568. Conclusion: There is significant positive correlation of BMI with AMD and AMA in general population suggesting people with low BMI are at an increased risk of developing SMAS