Abstract
Data mining projects increasingly require records about individuals to be linked across databases to facilitate advanced analytics. The process of linking records without revealing any sensitive or confidential information about the entities represented by these records is known as privacy-preserving record linkage (PPRL). Bloom filters are a popular PPRL technique to encode sensitive information while still enabling approximate linking of records. However, Bloom filter encoding can be vulnerable to attacks that can re-identify some encoded values from sets of Bloom filters. Existing attacks exploit that certain Bloom filters can occur frequently in an encoded database, and thus likely correspond to frequent plain-text values such as common names. We present a novel attack method based on a maximal frequent itemset mining technique which identifies frequently co-occurring bit positions in a set of Bloom filters. Our attack can re-identify encoded sensitive values even when all Bloom filters in an encoded database are unique. As our experiments on a real-world data set show, our attack can successfully re-identify values from encoded Bloom filters even in scenarios where previous attacks fail.
This work was funded by the Australian Research Council under DP130101801 and DP160101934. Peter Christen likes to acknowledge the support of ScaDS Dresden/Leipzig (BMBF grant 01IS14014B), where parts of this work were conducted.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: VLDB, Santiago de Chile (1994)
Bloom, B.: Space/time trade-offs in hash coding with allowable errors. Commun. ACM 13(7), 422–426 (1970)
Boyd, J.H., Randall, S.M., Ferrante, A.M.: Application of privacy-preserving techniques in operational record linkage centres. In: Gkoulalas-Divanis, A., Loukides, G. (eds.) Medical Data Privacy Handbook, pp. 267–287. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23633-9_11
Christen, P.: Data Matching - Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31164-2
Christen, P., Schnell, R., Vatsalan, D., Ranbaduge, T.: Efficient cryptanalysis of bloom filters for privacy-preserving record linkage. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10234, pp. 628–640. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57454-7_49
Durham, E.A., Kantarcioglu, M., Xue, Y., Toth, C., Kuzu, M., Malin, B.: Composite Bloom filters for secure record linkage. IEEE TKDE 26(12), 2956–2968 (2014)
Grahne, G., Zhu, J.: Fast algorithms for frequent itemset mining using FP-trees. IEEE TKDE 17(10), 1347–1362 (2005)
Hegland, M.: The Apriori algorithm - a tutorial. Math. Comput. Imaging Sci. Inf. Process. 11, 209–262 (2005)
Karapiperis, D., Gkoulalas-Divanis, A., Verykios, V.S.: FEDERAL: a framework for distance-aware privacy-preserving record linkage. IEEE TKDE 30(2), 292–304 (2017)
Kroll, M., Steinmetzer, S.: Automated cryptanalysis of bloom filter encryptions of databases with several personal identifiers. In: BIOSTEC, Lisbon (2015)
Kuzu, M., Kantarcioglu, M., Durham, E., Malin, B.: A constraint satisfaction cryptanalysis of bloom filters in private record linkage. In: Fischer-Hübner, S., Hopper, N. (eds.) PETS 2011. LNCS, vol. 6794, pp. 226–245. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22263-4_13
Kuzu, M., Kantarcioglu, M., Durham, E., Toth, C., Malin, B.: A practical approach to achieve private medical record linkage in light of public resources. JAMIA 20(2), 285–292 (2013)
Mitchell, W., Dewri, R., Thurimella, R., Roschke, M.: A graph traversal attack on Bloom filter-based medical data aggregation. IJBDI 4(4), 217–226 (2017)
Mitzenmacher, M., Upfal, E.: Probability and Computing: Randomized Algorithms and Probabilistic Analysis. Cambridge University Press, Cambridge (2005)
Niedermeyer, F., Steinmetzer, S., Kroll, M., Schnell, R.: Cryptanalysis of basic Bloom filters used for privacy preserving record linkage. JPC 6(2), 59–79 (2014)
Randall, S., Ferrante, A., Boyd, J., Bauer, J., Semmens, J.: Privacy-preserving record linkage on large real world datasets. JBI 50, 205–212 (2014)
Schnell, R., Bachteler, T., Reiher, J.: Privacy-preserving record linkage using Bloom filters. BMC Med. Inform. Decis. Making 9(1), 41 (2009)
Schnell, R., Borgs, C.: Randomized response and balanced Bloom filters for privacy preserving record linkage. In: ICDMW DINA, Barcelona (2016)
Vatsalan, D., Christen, P.: Privacy-preserving matching of similar patients. JBI 59, 285–298 (2016)
Vatsalan, D., Sehili, Z., Christen, P., Rahm, E.: Privacy-preserving record linkage for big data: current approaches and research challenges. In: Zomaya, A.Y., Sakr, S. (eds.) Handbook of Big Data Technologies, pp. 851–895. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-49340-4_25
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Christen, P., Vidanage, A., Ranbaduge, T., Schnell, R. (2018). Pattern-Mining Based Cryptanalysis of Bloom Filters for Privacy-Preserving Record Linkage. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4_42
Download citation
DOI: https://doi.org/10.1007/978-3-319-93040-4_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93039-8
Online ISBN: 978-3-319-93040-4
eBook Packages: Computer ScienceComputer Science (R0)