Description

本日紹介した論文の一覧

• Blockchain Technology: Core Mechanisms, Evolution, and Future
  Implementation Challenges
  http://arxiv.org/abs/2505.08772v1

• Kudzu: Fast and Simple High-Throughput BFT
  http://arxiv.org/abs/2505.08771v1

• Securing RAG: A Risk Assessment and Mitigation Framework
  http://arxiv.org/abs/2505.08728v1

• Comparative Analysis of Blockchain Systems
  http://arxiv.org/abs/2505.08652v1

• Cryptologic Techniques and Associated Risks in Public and Private
  Security. An Italian and European Union Perspective with an Overview of the
  Current Legal Framework
  http://arxiv.org/abs/2505.08650v1

• Information Leakage in Data Linkage
  http://arxiv.org/abs/2505.08596v1

• MUBox: A Critical Evaluation Framework of Deep Machine Unlearning
  http://arxiv.org/abs/2505.08576v1

• ROSA: Finding Backdoors with Fuzzing
  http://arxiv.org/abs/2505.08544v1

• Area Comparison of CHERIoT and PMP in Ibex
  http://arxiv.org/abs/2505.08541v1

• On the Account Security Risks Posed by Password Strength Meters
  http://arxiv.org/abs/2505.08292v1

• Where the Devil Hides: Deepfake Detectors Can No Longer Be Trusted
  http://arxiv.org/abs/2505.08255v1

• Privacy-Preserving Analytics for Smart Meter (AMI) Data: A Hybrid
  Approach to Comply with CPUC Privacy Regulations
  http://arxiv.org/abs/2505.08237v1

• Removing Watermarks with Partial Regeneration using Semantic Information
  http://arxiv.org/abs/2505.08234v1

• ABAC Lab: An Interactive Platform for Attribute-based Access Control
  Policy Analysis, Tools, and Datasets
  http://arxiv.org/abs/2505.08209v1

• LM-Scout: Analyzing the Security of Language Model Integration in
  Android Apps
  http://arxiv.org/abs/2505.08204v1

• GDNTT: an Area-Efficient Parallel NTT Accelerator Using Glitch-Driven
  Near-Memory Computing and Reconfigurable 10T SRAM
  http://arxiv.org/abs/2505.08162v1

• A Large-Scale Empirical Analysis of Custom GPTs' Vulnerabilities in the
  OpenAI Ecosystem
  http://arxiv.org/abs/2505.08148v1

• Mirror Mirror on the Wall, Have I Forgotten it All? A New Framework for
  Evaluating Machine Unlearning
  http://arxiv.org/abs/2505.08138v1

なお、ポッドキャスト内で紹介する内容は、各論文の概要を日本語で解説したもので、論文概要の著作権は論文著者に帰属します。