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hash algorithms

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Sanyou's Java Diary
Sanyou's Java Diary
Feb 5, 2024 · Information Security

Master the Top 5 Encryption Algorithms: MD5, SHA-256, DES, AES, RSA Explained

This article introduces the five most common encryption algorithms—MD5, SHA‑256, DES, AES, and RSA—explaining their classifications, security properties, typical use cases, and providing complete Java implementations for each, while also showing how they underpin HTTPS security.

Javaasymmetric encryptionencryption
0 likes · 18 min read
Master the Top 5 Encryption Algorithms: MD5, SHA-256, DES, AES, RSA Explained
IT Services Circle
IT Services Circle
May 8, 2022 · Information Security

An Introduction to Hashcat: Features, Usage, and Command Options

This article introduces Hashcat, the world’s fastest password‑recovery tool, outlines its extensive feature set, provides the project’s GitHub address, and explains how to download, install, and run basic commands with common options for various hash types and attack modes.

Command-lineGPU accelerationHashcat
0 likes · 4 min read
An Introduction to Hashcat: Features, Usage, and Command Options
Architect
Architect
Oct 18, 2021 · Fundamentals

Understanding Simhash: From Traditional Hash to Random Projection and LSH

This article explains the principles behind Simhash, covering the shortcomings of traditional hash functions, the use of cosine similarity, random projection for dimensionality reduction, locality‑sensitive hashing, random hyperplane hashing, implementation steps, query optimization with the pigeonhole principle, and the algorithm's limitations in short‑text scenarios.

Locality Sensitive HashingRandom ProjectionSimhash
0 likes · 18 min read
Understanding Simhash: From Traditional Hash to Random Projection and LSH
Tencent Cloud Developer
Tencent Cloud Developer
Jun 16, 2021 · Backend Development

Comparison of Four Consistent Hashing Algorithms: Ketama, Rendezvous, Jump Consistent Hash, and Maglev

The article compares four consistent‑hashing algorithms—Ketama’s ring with virtual nodes, Rendezvous’s highest‑random‑weight method, Google’s Jump Consistent Hash, and Maglev’s lookup‑table approach—evaluating their balance, monotonicity, stability, scalability, and time complexity, and concludes that Ketama and Jump offer the best overall trade‑off.

Algorithm Comparisonconsistent hashingdistributed systems
0 likes · 23 min read
Comparison of Four Consistent Hashing Algorithms: Ketama, Rendezvous, Jump Consistent Hash, and Maglev
Aikesheng Open Source Community
Aikesheng Open Source Community
Sep 12, 2019 · Databases

Comparison of Consistent Hash (Ring Hash) and JumpStringHash for DBLE Sharding

This article explains the principles, characteristics, and performance trade‑offs of the consistent‑hash (ring‑hash) and jumpstringhash sharding algorithms used by DBLE, presents test results on variance, latency and data balance, and concludes why DBLE prefers jumpstringhash.

Consistent HashPerformance TestingSharding
0 likes · 7 min read
Comparison of Consistent Hash (Ring Hash) and JumpStringHash for DBLE Sharding
360 Quality & Efficiency
360 Quality & Efficiency
Oct 19, 2018 · Big Data

Information Fingerprint and Simhash Algorithm for Large-Scale Duplicate Detection

This article explains the concept of information fingerprints, compares traditional set‑equality methods, introduces the Simhash algorithm for high‑dimensional text similarity reduction, and demonstrates how partitioned 64‑bit fingerprints enable efficient duplicate detection on massive web data.

Big DataDuplicate DetectionSimhash
0 likes · 6 min read
Information Fingerprint and Simhash Algorithm for Large-Scale Duplicate Detection
Xianyu Technology
Xianyu Technology
Apr 26, 2018 · Artificial Intelligence

Client‑Side Image Similarity Computation: Methods, Experiments, and Findings

This study compares hash‑based, CNN‑based, and local‑feature methods for client‑side image similarity detection in e‑commerce, showing that while hash methods are fast and CNNs are accurate but costly, the Hessian‑Affine detector combined with SIFT descriptors delivers the optimal balance of computational efficiency, robustness to transformations, and high recall/precision for on‑device duplicate filtering.

CNNFeature ExtractionMobile Computing
0 likes · 11 min read
Client‑Side Image Similarity Computation: Methods, Experiments, and Findings