Privacy-Enhanced Fraud Detection with Bloom Filters

Arp, Daniel J.; Quiring, Erwin; Krueger, Tammo; Dragiev, Stanimir; Rieck, Konrad

doi:10.1007/978-3-030-01701-9_22

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…We thus test our candidate's efficiency (256 and 512 bit if available), using two widely used, modern C++ libraries, crypto++ and botan/2. 2 The most efficient 3 -and thus our choice -is the botan/2 implementation of BLAKE2b with 512 bit.…”

Section: Cryptographic Hashmentioning

confidence: 99%

“…The security and privacy properties of BFs are studied in e.g. [3,4,20]. Additionally, several hardening techniques for BFs are sugested in [10,21], such as salting, noise injection, and a random selection of hash functions.…”

Section: Bloom Filter Hardeningmentioning

confidence: 99%

“…An additional advantage of botan/2 is its security review and improvement by the German Federal Office for Information Security 3. Even though popular SHA1 and MD5 are excluded, because they are considered broken, they are outperformed by BLAKE2b anyways.…”

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confidence: 99%

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Privacy-Enhanced Robust Image Hashing with Bloom Filters

Breidenbach

Steinebach

Liu

2021

JCSANDM

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Robust image hashes are used to detect known illegal images, even after image processing. This is, for example, interesting for a forensic investigation, or for a company to protect their employees and customers by filtering content. The disadvantage of robust hashes is that they leak structural information of the pictures, which can lead to privacy issues. Our scientific contribution is to extend a robust image hash with privacy protection. We thus introduce and discuss such a privacy-preserving concept. The approach uses a probabilistic data structure -- known as Bloom filter -- to store robust image hashes. Bloom filter store elements by mapping hashes of each element to an internal data structure. We choose a cryptographic hash function to one-way encrypt and store elements. The privacy of the inserted elements is thus protected. We evaluate our implementation, and compare it to its underlying robust image hashing algorithm. Thereby, we show the cost with respect to error rates for introducing a privacy protection into robust hashing. Finally, we discuss our approach's results and usability, and suggest possible future improvements.

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Section: Cryptographic Hashmentioning

confidence: 99%