Publications

Privacy-enhanced robust image hashing with bloom filters

AuthorBreidenbach, Uwe; Steinebach, Martin; Liu, Huajian
Date2020
TypeConference Paper
AbstractRobust 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.
ConferenceInternational Conference on Availability, Reliability and Security (ARES) <15, 2020, Online>
PartVolkamer, M.: ARES 2020, 15th International Conference on Availability, Reliability and Security: August 25 - August 28, 2020, All-digital Conference. New York: ACM, 2020, Art. 56, 10 pp.