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research#cryptography📝 BlogAnalyzed: Jan 4, 2026 15:21

ChatGPT Explores Code-Based CSPRNG Construction

Published:Jan 4, 2026 07:57
1 min read
Qiita ChatGPT

Analysis

This article, seemingly generated by or about ChatGPT, discusses the construction of cryptographically secure pseudorandom number generators (CSPRNGs) using code-based one-way functions. The exploration of such advanced cryptographic primitives highlights the potential of AI in contributing to security research, but the actual novelty and rigor of the approach require further scrutiny. The reliance on code-based cryptography suggests a focus on post-quantum security considerations.
Reference

“疑似乱数生成器（Pseudorandom Generator, PRG）は暗号の中核的構成要素であり、暗号化、署名、鍵生成など、ほぼすべての暗号技術に利用され...”

PermalinkQiita ChatGPT
Technology#AI Content Verification📝 BlogAnalyzed: Jan 3, 2026 18:14

Proposed New Media Format to Combat AI-Generated Content

Published:Jan 3, 2026 18:12
1 min read
r/artificial

Analysis

The article proposes a technical solution to the problem of AI-generated "slop" (likely referring to low-quality or misleading content) by embedding a cryptographic hash within media files. This hash would act as a signature, allowing platforms to verify the authenticity of the content. The simplicity of the proposed solution is appealing, but its effectiveness hinges on widespread adoption and the ability of AI to generate content that can bypass the hash verification. The article lacks details on the technical implementation, potential vulnerabilities, and the challenges of enforcing such a system across various platforms.
Reference

“Any social platform should implement a common new format that would embed hash that AI would generate so people know if its fake or not. If there is no signature -> media cant be published. Easy.”

Permalinkr/artificial
Research Paper Analysis#Cryptography, RSA, Number Theory🔬 ResearchAnalyzed: Jan 3, 2026 17:11

Correctness of Extended RSA Analysis

Published:Dec 31, 2025 00:26
1 min read
ArXiv

Analysis

This paper focuses on the mathematical correctness of RSA-like schemes, specifically exploring how the choice of N (a core component of RSA) can be extended beyond standard criteria. It aims to provide explicit conditions for valid N values, differing from conventional proofs. The paper's significance lies in potentially broadening the understanding of RSA's mathematical foundations and exploring variations in its implementation, although it explicitly excludes cryptographic security considerations.
Reference

“The paper derives explicit conditions that determine when certain values of N are valid for the encryption scheme.”

PermalinkArXiv
Research Paper#Blockchain, Security, Border Control🔬 ResearchAnalyzed: Jan 3, 2026 15:51

GateChain: Blockchain for Border Control

Published:Dec 30, 2025 18:58
1 min read
ArXiv

Analysis

This paper proposes a blockchain-based solution, GateChain, to improve the security and efficiency of country entry/exit record management. It addresses the limitations of traditional centralized systems by leveraging blockchain's immutability, transparency, and distributed nature. The application's focus on real-time access control and verification for authorized institutions is a key benefit.
Reference

“GateChain aims to enhance data integrity, reliability, and transparency by recording entry and exit events on a distributed, immutable, and cryptographically verifiable ledger.”

PermalinkArXiv
Survey#Cybersecurity, Power Side-Channel Attacks, Security🔬 ResearchAnalyzed: Jan 3, 2026 18:35

Application-Specific Power Side-Channel Attacks and Countermeasures Survey

Published:Dec 29, 2025 17:13
1 min read
ArXiv

Analysis

This survey paper is important because it moves beyond the traditional focus on cryptographic implementations in power side-channel attacks. It explores the application of these attacks and countermeasures in diverse domains like machine learning, user behavior analysis, and instruction-level disassembly, highlighting the broader implications of power analysis in cybersecurity.
Reference

“This survey aims to classify recent power side-channel attacks and provide a comprehensive comparison based on application-specific considerations.”

PermalinkArXiv
Research Paper#AI Security, Deep Learning, Dropout, Zero-Knowledge Proofs🔬 ResearchAnalyzed: Jan 3, 2026 19:57

Verifiable Dropout: Ensuring Integrity in AI Training

Published:Dec 27, 2025 09:14
1 min read
ArXiv

Analysis

This paper addresses a critical vulnerability in cloud-based AI training: the potential for malicious manipulation hidden within the inherent randomness of stochastic operations like dropout. By introducing Verifiable Dropout, the authors propose a privacy-preserving mechanism using zero-knowledge proofs to ensure the integrity of these operations. This is significant because it allows for post-hoc auditing of training steps, preventing attackers from exploiting the non-determinism of deep learning for malicious purposes while preserving data confidentiality. The paper's contribution lies in providing a solution to a real-world security concern in AI training.
Reference

“Our approach binds dropout masks to a deterministic, cryptographically verifiable seed and proves the correct execution of the dropout operation.”

PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 09:31

Key Length-Oriented Classification of Lightweight Cryptographic Algorithms for IoT Security

Published:Dec 23, 2025 22:54
1 min read
ArXiv

Analysis

This article, sourced from ArXiv, focuses on classifying lightweight cryptographic algorithms based on key length, specifically for the context of IoT security. The research likely aims to provide a structured understanding of different algorithms and their suitability for resource-constrained IoT devices. The focus on key length suggests an emphasis on security strength and computational efficiency trade-offs. The ArXiv source indicates this is likely a peer-reviewed research paper.
Reference

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PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 08:13

Optimistic TEE-Rollups: A Hybrid Architecture for Scalable and Verifiable Generative AI Inference on Blockchain

Published:Dec 23, 2025 09:16
1 min read
ArXiv

Analysis

This article proposes a hybrid architecture combining Trusted Execution Environments (TEEs) and rollups to enable scalable and verifiable generative AI inference on blockchain. The approach aims to address the computational and verification challenges of running complex AI models on-chain. The use of TEEs provides a secure environment for computation, while rollups facilitate scalability. The paper likely details the architecture, its security properties, and performance evaluations. The focus on verifiable inference is crucial for trust and transparency in AI applications.
Reference

“The article likely explores how TEEs can securely execute AI models, and how rollups can aggregate and verify the results, potentially using cryptographic proofs.”

PermalinkArXiv
Research#Cryptography🔬 ResearchAnalyzed: Jan 10, 2026 08:49

Quantum-Resistant Cryptography: Securing Cybersecurity's Future

Published:Dec 22, 2025 03:47
1 min read
ArXiv

Analysis

This article from ArXiv highlights the critical need for quantum-resistant cryptographic models in the face of evolving cybersecurity threats. It underscores the urgency of developing and implementing new security protocols to safeguard against future quantum computing attacks.
Reference

“The article's source is ArXiv, indicating a focus on academic research.”

PermalinkArXiv
Research#Blockchain🔬 ResearchAnalyzed: Jan 10, 2026 11:09

Quantum Threat to Blockchain: A Security and Performance Analysis

Published:Dec 15, 2025 13:48
1 min read
ArXiv

Analysis

This ArXiv paper likely explores the vulnerabilities of blockchain technology to attacks from quantum computers, analyzing how quantum computing could compromise existing cryptographic methods used in blockchains. The study probably also assesses the performance impact of implementing post-quantum cryptographic solutions.
Reference

“The paper focuses on how post-quantum attackers reshape blockchain security and performance.”

PermalinkArXiv
Research#Cryptography🔬 ResearchAnalyzed: Jan 10, 2026 11:29

Mage: AI Cracks Elliptic Curve Cryptography

Published:Dec 13, 2025 22:45
1 min read
ArXiv

Analysis

This research suggests a potential vulnerability in widely used cryptographic systems, highlighting the need for ongoing evaluation and potential updates to existing security protocols. The utilization of cross-axis transformers demonstrates a novel approach to breaking these defenses.
Reference

“The research is sourced from ArXiv.”

PermalinkArXiv
Research#Cryptography🔬 ResearchAnalyzed: Jan 10, 2026 12:30

New Pseudorandom Codes Emerge from Permutation Puzzles

Published:Dec 9, 2025 18:53
1 min read
ArXiv

Analysis

This article discusses a novel approach to generating improved pseudorandom codes using a permutation-based puzzle method. While the specifics of the method are not detailed, the implication is a potential advancement in cryptographic applications or simulations where randomness is critical.
Reference

“The source is ArXiv, suggesting the article is a scientific publication.”

PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 08:48

Combined Quantum and Post-Quantum Security Performance Under Finite Keys

Published:Dec 4, 2025 03:52
1 min read
ArXiv

Analysis

This article, sourced from ArXiv, likely presents research on the performance of cryptographic systems that are designed to be secure against both quantum and classical attacks, specifically focusing on the impact of using finite key sizes. The research probably analyzes the trade-offs and efficiencies of these combined security approaches.

Key Takeaways

    Reference

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    PermalinkArXiv
    Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 08:09

    CryptoQA: A Large-scale Question-answering Dataset for AI-assisted Cryptography

    Published:Dec 2, 2025 10:35
    1 min read
    ArXiv

    Analysis

    This article introduces CryptoQA, a new dataset designed to facilitate AI-assisted cryptography research. The focus is on question-answering, suggesting the dataset is structured to evaluate AI models' ability to understand and respond to cryptographic queries. The scale of the dataset is highlighted, implying a significant resource for training and evaluating AI systems in this domain. The source, ArXiv, indicates this is likely a research paper.
    Reference

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    PermalinkArXiv
    Research#Distribution Testing🔬 ResearchAnalyzed: Jan 10, 2026 14:10

    Interactive Proofs Advance Distribution Testing

    Published:Nov 27, 2025 05:30
    1 min read
    ArXiv

    Analysis

    This ArXiv article likely presents novel research in theoretical computer science, focusing on the intersection of interactive proof systems and distribution testing. The research could offer improvements to the efficiency or capabilities of algorithms used to analyze data distributions.
    Reference

    “The article is from ArXiv, indicating it is likely a pre-print of a research paper.”

    PermalinkArXiv
    Research#Cryptography👥 CommunityAnalyzed: Jan 3, 2026 06:28

    Machine Learning on Encrypted Data Without Decrypting It

    Published:Nov 26, 2019 14:45
    1 min read
    Hacker News

    Analysis

    This headline suggests a significant advancement in data privacy and security. The ability to perform machine learning on encrypted data without decryption has implications for various fields, including healthcare, finance, and national security. It implies the use of techniques like homomorphic encryption or secure multi-party computation.
    Reference

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    PermalinkHacker News