arXiv subjects

Sayantan Mukherjee

Publications and source records attributed to Sayantan Mukherjee.

5 recordsLinked to original sources

arXiv2026

Large Universe Subset Predicate Encryption with IND-CCA Security (with Constant-size Ciphertext and Keys)

Katz et al. (CANS'17) introduced Subset Predicate Encryption (SPE). This scheme is a generalization of broadcast encryption as it emulates the \emph{subset containment} predicate in the encrypted domain. They proposed two selectively IND-CPA secure SPE constructions in the small universe setting. They also showed some black-box transformations of SPE to well-known primitives like WIBE and ABE to establish the richness of the SPE structure. Chatterjee and Mukherjee (RSA'19) proposed two SPE constructions in the large-universe setting. Their first construction achieved constant-size ciphertexts and secret keys, but it is proven secure in a restricted version of selective security. Although the second construction achieves adaptive security, the ciphertext size depends on the size of the data-attribute set. Furthermore, neither of these two constructions achieves CCA security. In this work, we propose the first large-universe CCA-secure subset predicate encryption with constant-size ciphertext and secret keys. We prove this construction achieves standard selective security under the standard subgroup decision problems. Finally, we transform our extremely efficient SPE into the first CCA-secure WIBE, WKD-IBE, etc., with constant-size ciphertexts and secret keys via black-box transformations.

cs.CR↗

arXiv2026

A Broadcast Authenticated Encryption with Keyword Search in the Standard Model: Tightly Secure in Multi-User, Multi-Challenge Settings

However, no known work considered the functionality requirement in its most realistic setting. We propose a new security definition of BAEKS in the multi-user (with adaptive corruptions) and multi-challenge (both in terms of ciphertext and trapdoor in an interleaved manner) settings. We also study the question of the unforgeability of BAEKS. In fact, our strong hiding requirement already implies a significant amount of unforgeability. We then propose a new BAEKS construction in the bilinear pairing groups. We prove this scheme achieves adaptive tight full-hiding security under (almost) standard MDDH assumptions. Restricting our BAEKS construction for a single receiver also gives an efficient and tightly secure PAEKS construction. We further run experiments to implement and evaluate our scheme.

cs.CR↗

arXiv2026

Two-stage Convolutional Neural Network for pseudo six-dimensional phase space reconstruction

In particle accelerators, broad characterization of the six-dimensional (6D) beam phase space is crucial but difficult to obtain with conventional beam diagnostics. We develop a two-stage convolutional neural network (CNN) that reconstructs the 6D phase space from only sixteen transverse $x-y$ screen images taken at a place with dispersion by different phase space rotation angles. The model is trained with simulation data of KEK-Accelerator Test Facility (ATF) injector with ASTRA. The real-space images in the chicane orbit at the KEK-ATF injector were acquired by varying the RF phase of the RF electron gun and the solenoid magnetic field. From these data, we reconstructed a pseudo 6D phase space distribution at the cathode surface, expressed through 15 two-dimensional (2D) distributions covering all pairwise coordinate combinations. The time width and spatial spread of the electron beam at the cathode showed values consistent with the measured values at KEK-ATF. Compared to existing 6D beam imaging measurement techniques such as tomography, it significantly reduces measurement time and required computational resources, enabling the provision of a more practical 6D phase space measurement method.

hep-ex↗

arXiv2025

Assessing the influence of social media feedback on traveler's future trip-planning behavior: A multi-model machine learning approach

With the surge of domestic tourism in India and the influence of social media on young tourists, this paper aims to address the research question on how "social return" - responses received on social media sharing - of recent trip details can influence decision-making for short-term future travels. The paper develops a multi-model framework to build a predictive machine learning model that establishes a relationship between a traveler's social return, various social media usage, trip-related factors, and her future trip-planning behavior. The primary data was collected via a survey from Indian tourists. After data cleaning, the imbalance in the data was addressed using a robust oversampling method, and the reliability of the predictive model was ensured by applying a Monte Carlo cross-validation technique. The results suggest at least 75% overall accuracy in predicting the influence of social return on changing the future trip plan. Moreover, the model fit results provide crucial practical implications for the domestic tourism sector in India with future research directions concerning social media, destination marketing, smart tourism, heritage tourism, etc.

cs.SI↗

arXiv2022

Segmentation of Blood Vessels, Optic Disc Localization, Detection of Exudates and Diabetic Retinopathy Diagnosis from Digital Fundus Images

Diabetic Retinopathy (DR) is a complication of long-standing, unchecked diabetes and one of the leading causes of blindness in the world. This paper focuses on improved and robust methods to extract some of the features of DR, viz. Blood Vessels and Exudates. Blood vessels are segmented using multiple morphological and thresholding operations. For the segmentation of exudates, k-means clustering and contour detection on the original images are used. Extensive noise reduction is performed to remove false positives from the vessel segmentation algorithm's results. The localization of Optic Disc using k-means clustering and template matching is also performed. Lastly, this paper presents a Deep Convolutional Neural Network (DCNN) model with 14 Convolutional Layers and 2 Fully Connected Layers, for the automatic, binary diagnosis of DR. The vessel segmentation, optic disc localization and DCNN achieve accuracies of 95.93%, 98.77% and 75.73% respectively. The source code and pre-trained model are available https://github.com/Sohambasu07/DR_2021

eess.IV↗