Privacy-Enhanced Robust Image Hashing with Bloom Filters




robust hashing, perceptual hashing, hashing, fingerprinting, forensics, Privacy-preserving


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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Author Biographies

Uwe Breidenbach, Technische Universität Darmstadt, Germany

Uwe Breidenbach. In 2016 Uwe Breidenbach graduated with a BSc in Computer Science, and in 2020 with a MSc in IT-Security, as well as a MSc in Computer Science from Technische Universität Darmstadt, Germany. He wrote his bachelor and master thesis at the Fraunhofer Institute for Secure Information Technology, Darmstadt, Germany. In addition, the master thesis was written in cooperation with the Faculty of Engineering of Mahidol University, Thailand. His main interest of research and work include penetration testing, ethical hacking, network security, digital forensics, and privacy protection.

Martin Steinebach, Fraunhofer Institute for Secure Information Technology SIT, Germany

Martin Steinebach is the manager of the Media Security and IT Forensics division at Fraunhofer SIT. From 2003 to 2007 he was the manager of the Media Security in IT division at Fraunhofer IPSI. He studied computer science at the Technical University of Darmstadt and finished his diploma thesis on copyright protection for digital audio in 1999. In 2003 he received his PhD at the Technical University of Darmstadt for his work on digital audio watermarking. In 2016 he became honorary professor at the TU Darmstadt. He gives lectures on Multimedia Security as well as Civil Security. He is Principle Investigator at ATHENE and represents IT Forensics and AI Security. Before he was Principle Investigator at CASED with the topics Multimedia Security and IT Forensics. In 2012 his work on robust image hashing for detection of child pornography reached the second rank of the Deutscher IT Sicherheitspreis, an award funded by Host Görtz.

Huajian Liu, Fraunhofer Institute for Secure Information Technology SIT, Germany

Huajian Liu received his B.S. and M.S. degrees in electronic engineering from Dalian University of Technology, China, in 1999 and 2002, respectively, and his Ph.D. degree in computer science from Technical University Darmstadt, Germany, in 2008. He is currently a senior research scientist at Fraunhofer Institute for Secure Information Technology (SIT). His major research interests include information security, digital watermarking, robust hashing and digital forensics.


A. Aminnezhad and A. Dehghantanha. A survey on privacy issues in digital forensics. International Journal of Cyber-Security and Digital Forensics (IJCSDF), 3:183–199, 2014. ISSN 2305-0012. URL

F. Armknecht and A. Dewald. Privacy-preserving email forensics. Digital Investigation, 14:S127–S136, Aug. 2015. ISSN 1742-2876. doi: 10.1016/j.diin.2015.05.003.

D. Arp, E. Quiring, T. Krueger, S. Dragiev, and K. Rieck. Privacy-Enhanced Fraud Detection with Bloom Filters. In Security and Privacy in Communication Networks, volume 254, pages 396–415. Springer International Publishing, Cham, 2018. ISBN 978-3-030-01700-2 978-3-030-01701-9. doi: 10.1007/978-3-030-01701-9_22.

G. Bianchi, L. Bracciale, and P. Loreti. ”Better Than Nothing” Privacy with Bloom Filters: To What Extent? In Privacy in Statistical Databases, volume 7556, pages 348–363. Springer Berlin Heidelberg, Berlin, Heidelberg, 2012. ISBN 978-3-642-33627-0. doi: 10.1007/978-3-642-33627-0_27.

B. H. Bloom. Space/Time Trade-offs in Hash Coding with Allowable Errors. Commun. ACM, 13(7):422–426, July 1970. ISSN 0001-0782. doi: 10/ckwxm7.

F. Breitinger, H. Liu, C. Winter, H. Baier, A. Rybalchenko, and M. Steinebach. Towards a Process Model for Hash Functions in Digital Forensics. In Digital Forensics and Cyber Crime, volume 132, pages 170–186. Springer International Publishing, Cham, 2014. ISBN 978-3-319-14288-3 978-3-319-14289-0. doi: 10.1007/978-3-319-14289-0_12.

J. Bringer, H. Chabanne, and A. Patey. SHADE: Secure HAmming DistancE Computation from Oblivious Transfer. In Financial Cryptography and Data Security, volume 7862, pages 164–176. Springer Berlin Heidelberg, Berlin, Heidelberg, 2013. ISBN 978-3-642-41319-3 978-3-642-41320-9. doi: 10.1007/978-3-642-41320-9_11.

J. Bringer, H. Chabanne, M. Favre, A. Patey, T. Schneider, and M. Zohner. GSHADE: Faster privacy-preserving distance computation and biometric identification. In Proceedings of the 2nd ACM Workshop on Information Hiding and Multimedia Security - IH&MMSec ’14, pages 187–198, Salzburg, Austria, 2014. ACM Press. ISBN 978-1-4503-2647-6. doi: 10.1145/2600918.2600922.

S. G. Choi, K.-W. Hwang, J. Katz, T. Malkin, and D. Rubenstein. Secure Multi-Party Computation of Boolean Circuits with Applications to Privacy in On-Line Marketplaces. In Topics in Cryptology – CT-RSA 2012, Lecture Notes in Computer Science, pages 416–432, Berlin, Heidelberg, 2012. Springer. ISBN 978-3-642-27954-6. doi: 10/ggfwhg.

P. Christen, R. Schnell, D. Vatsalan, and T. Ranbaduge. Efficient Cryptanalysis of Bloom Filters for Privacy-Preserving Record Linkage. In Advances in Knowledge Discovery and Data Mining, volume 10234, pages 628–640. Springer International Publishing, Cham, 2017. ISBN 978-3-319-57453-0 978-3-319-57454-7. doi: 10.1007/978-3-319-57454-7_49.

R. Cramer and I. Damgå rd. Zero-knowledge proofs for finite field arithmetic, or: Can zero-knowledge be for free? In Advances in Cryptology — CRYPTO ’98, Lecture Notes in Computer Science, pages 424–441. Springer Berlin Heidelberg, 1998. ISBN 978-3-540-68462-6.

M. J. Dworkin. SHA-3 Standard: Permutation-Based Hash and Extendable-Output Functions. Techreport 202, National Institute of Standards and Technology, Aug. 2015.

Z. Erkin, M. Franz, J. Guajardo, S. Katzenbeisser, I. Lagendijk, and T. Toft. Privacy-Preserving Face Recognition. In Privacy Enhancing Technologies, Lecture Notes in Computer Science, pages 235–253, Berlin, Heidelberg, 2009. Springer. ISBN 978-3-642-03168-7. doi: 10/ddznj8.

A. Gervais, S. Capkun, G. O. Karame, and D. Gruber. On the privacy provisions of Bloom filters in lightweight bitcoin clients. In Proceedings of the 30th Annual Computer Security Applications Conference on - ACSAC ’14, pages 326–335, New Orleans, Louisiana, 2014. ACM Press. ISBN 978-1-4503-3005-3. doi: 10.1145/2664243.2664267.

O. Goldreich, S. Micali, and A. Wigderson. How to Play ANY Mental Game. In Proceedings of the Nineteenth Annual ACM Symposium on Theory of Computing, STOC ’87, pages 218–229, New York, NY, USA, 1987. ACM. ISBN 978-0-89791-221-1. doi: 10/bpf74b.

R. W. Hamming. Error detecting and error correcting codes. The Bell System Technical Journal, 29(2):147–160, Apr. 1950. ISSN 0005-8580. doi: 10/gcz6kp.

S. Hou, T. Uehara, S. M. Yiu, L. C. K. Hui, and K. P. Chow. Privacy Preserving Confidential Forensic Investigation for Shared or Remote Servers. In 2011 Seventh International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pages 378–383, Oct. 2011. doi: 10.1109/IIHMSP.2011.28.

Y. Huang, D. Evans, J. Katz, and L. Malka. Faster Secure Two-party Computation Using Garbled Circuits. In Proceedings of the 20th USENIX Conference on Security, SEC’11, pages 35–35, Berkeley, CA, USA, 2011. USENIX Association. URL

A. Jarrous and B. Pinkas. Secure Hamming Distance Based Computation and Its Applications. In RoboCup 2001: Robot Soccer World Cup V, volume 2377, pages 107–124. Springer Berlin Heidelberg, Berlin, Heidelberg, 2009. ISBN 978-3-540-43912-7 978-3-540-45603-2. doi: 10.1007/978-3-642-01957-9_7.

K. Kanemura, K. Toyoda, and T. Ohtsuki. Design of privacy-preserving mobile Bitcoin client based on γ

-deniability enabled bloom filter. In 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pages 1–6, Montreal, QC, Oct. 2017. IEEE. ISBN 978-1-5386-3529-2 978-1-5386-3531-5. doi: 10.1109/PIMRC.2017.8292537.

M. Kroll and S. Steinmetzer. Automated Cryptanalysis of Bloom Filter Encryptions of Health Records. In Proceedings of the International Conference on Health Informatics, volume 1, pages 5–13, Lisbon, Jan. 2015. SciTePress. ISBN 978-989-758-068-0. doi: 10/gf5hd6.

F. Y. W. Law, P. P. F. Chan, S. M. Yiu, K. P. Chow, M. Y. K. Kwan, H. K. S. Tse, and P. K. Y. Lai. Protecting Digital Data Privacy in Computer Forensic Examination. In 2011 Sixth IEEE International Workshop on Systematic Approaches to Digital Forensic Engineering, pages 1–6, May 2011. doi: 10.1109/SADFE.2011.15.

M-J. Saarinen and J-P. Aumasson. The BLAKE2 Cryptographic Hash and Message Authentication Code (MAC). Technical Report RFC7693, RFC Editor, Nov. 2015.

M. Naor, B. Pinkas, and B. Pinkas. Efficient Oblivious Transfer Protocols. In Proceedings of the Twelfth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA ’01, pages 448–457, Philadelphia, PA, USA, 2001. Society for Industrial and Applied Mathematics. ISBN 978-0-89871-490-6. URL

J. R. Nechvatal, E. B. Barker, L. E. Bassham, W. E. Burr, M. J. Dworkin, J. Foti, and E. Roback. Report on the Development of the Advanced Encryption Standard (AES). Journal of Research (NIST JRES) -, 106 No. 3, June 2001. URL

M. Osadchy, B. Pinkas, A. Jarrous, and B. Moskovich. SCiFI - A System for Secure Face Identification. In 2010 IEEE Symposium on Security and Privacy, pages 239–254, Oakland, CA, USA, 2010. IEEE. ISBN 978-1-4244-6894-2. doi: 10.1109/SP.2010.39.

B. Preneel. Cryptographic hash functions. European Transactions on Telecommunications, 5(4):431–448, 1994. ISSN 1541-8251. doi: 10/ch25dn.

J. S. Seo, J. Haitsma, T. Kalker, and C. D. Yoo. A robust image fingerprinting system using the Radon transform. Signal Processing: Image Communication, 19(4):325–339, Apr. 2004. ISSN 0923-5965. doi: 10.1016/j.image.2003.12.001.

M. Steinebach. Robust Hashing for Efficient Forensic Analysis of Image Sets. In Digital Forensics and Cyber Crime, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, pages 180–187. Springer Berlin Heidelberg, 2012. ISBN 978-3-642-35515-8. doi: 10/gf5hd4.

M. Steinebach, H. Liu, and Y. Yannikos. ForBild: Efficient Robust Image Hashing. In Media Watermarking, Security and Forensics 2012, volume 8303, page 83030O. International Society for Optics and Photonics, Feb. 2012. doi: 10.1117/12.907856.

M. Steinebach, H. Liu, and Y. Yannikos. Efficient Cropping-Resistant Robust Image Hashing. In 2014 Ninth International Conference on Availability, Reliability and Security, pages 579–585, Sept. 2014. doi: 10.1109/ARES.2014.85.

M. Steinebach, S. Lutz, and H. Liu. Privacy and Robust Hashes. In Proceedings of the 14th International Conference on Availability, Reliability and Security - ARES ’19, pages 1–8, Canterbury, CA, United Kingdom, 2019. ACM Press. ISBN 978-1-4503-7164-3. doi: 10/ggwght.

United Nations. Universal Declaration of Human Rights, Dec. 1948. URL

Uwe Breidenbach. Privacy-Enhanced Robust Image Hashing with Bloom Filters. master, TU Darmstadt, Jan. 2020.

C. Winter, M. Steinebach, and Y. Yannikos. Fast indexing strategies for robust image hashes. Digital Investigation, 11:S27–S35, May 2014. ISSN 17422876. doi: 10.1016/j.diin.2014.03.004.

B. Yang, F. Gu, and X. Niu. Block Mean Value Based Image Perceptual Hashing. In 2006 International Conference on Intelligent Information Hiding and Multimedia, pages 167–172, Dec. 2006. doi: 10.1109/IIH-MSP.2006.265125.

A. C.-C. Yao. How to generate and exchange secrets. In 27th Annual Symposium on Foundations of Computer Science (Sfcs 1986), pages 162–167, Oct. 1986. doi: 10/dnw7tw.






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