Digital Forensics and Cyber Crime. 4th International Conference, ICDF2C 2012, Lafayette, IN, USA, October 25-26, 2012, Revised Selected Papers

Research Article

Similarity Preserving Hashing: Eligible Properties and a New Algorithm

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  • @INPROCEEDINGS{10.1007/978-3-642-39891-9_11,
        author={Frank Breitinger and Harald Baier},
        title={Similarity Preserving Hashing: Eligible Properties and a New Algorithm 
                },
        proceedings={Digital Forensics and Cyber Crime. 4th International Conference, ICDF2C 2012, Lafayette, IN, USA, October 25-26, 2012, Revised Selected Papers},
        proceedings_a={ICDF2C},
        year={2013},
        month={10},
        keywords={Digital forensics Similarity Preserving Hashing fuzzy hashing 
                    
                   properties of Similarity Preserving Hashing},
        doi={10.1007/978-3-642-39891-9_11}
    }
    
  • Frank Breitinger
    Harald Baier
    Year: 2013
    Similarity Preserving Hashing: Eligible Properties and a New Algorithm
    ICDF2C
    Springer
    DOI: 10.1007/978-3-642-39891-9_11
Frank Breitinger1,*, Harald Baier1,*
  • 1: Hochschule Darmstadt
*Contact email: frank.breitinger@h-da.de, harald.baier@h-da.de

Abstract

Hash functions are a widespread class of functions in computer science and used in several applications, e.g. in computer forensics to identify known files. One basic property of cryptographic Hash Functions is the avalanche effect that causes a significantly different output if an input is changed slightly. As some applications also need to identify similar files (e.g. spam/virus detection) this raised the need for . In recent years, several approaches came up, all with different namings, properties, strengths and weaknesses which is due to a missing definition.