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A novel cryptographic protocol for privacy preserving classification over distributed encrypted databases

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Abstract

Recent progression in Information Technology facilitated the collection and storage of large amounts of data to be accessed by multiple parties in a distributed manner. Privacy is an important concern while mining sensitive data. In a distributed data scenario, when the data is available in encrypted form, mining it without sharing original data among the involved parties is a challenging task. One of the activities in privacy preserving data mining is privacy preserving data classification. In this work, we propose a privacy preserving \(k\)-NN data classification technique for distributed encrypted databases. Our classification approach uses a private Jaccard similarity measure, which is based on privacy equality testing protocol. We also discuss the security analysis of the proposed protocol with respect to various cryptographic attacks.

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References

  1. Tan P-N, Steinbach M, Kumar V (2006) Introduction to data mining. Addison-Wesley, Boston

    Google Scholar 

  2. Kumar A, Madanu M, Prakash H, Jonnavithula L, Aravilli SR Advaita: big duplicity detection system. https://arxiv.org/abs/2001.10376

  3. Kaosar MG, Paulet R, Yi X (2012) Fully homomorphic encryption based two-party association rule mining. Data Knowl Eng 76–78:1–15

    Article  Google Scholar 

  4. Clifton C, Kantarcioglu M, Vaidya J (2002) Defining privacy for data mining. In: National science foundation workshop on next generation data mining

  5. Clifton C, Kantarcioglu M, Vaidya J, Lin X, Zhu MY (2002) Tools for privacy preserving distributed data mining. SIGKDD Explor Newsl 4:28–34

    Article  Google Scholar 

  6. Bellovin SM, Merritt M (1993) Augmented encrypted key exchange: a password-based protocol secure against dictionary attacks and password file compromise. In: Proceedings of the 1st ACM conference on computer and communications security. ACM, pp 244–250

  7. Cunha M, Mendes R, Vilela JP (2021) A survey of privacy-preserving mechanisms for heterogeneous data types. Computer science review, vol 41. Elsevier, Amsterdam, p 100403

    Google Scholar 

  8. Aldeen YAAS, Salleh M, Razzaque MA (2015) A comprehensive review on privacy preserving data mining. Springerplus 4:694

    Article  Google Scholar 

  9. Zhang N, Wang S, Zhao W (2005) A new scheme on privacy-preserving data classification. In: Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery in data mining, pp 374–383. ACM

  10. Wang Q, Datta KS, Sivakumar K (2003) On the privacy preserving properties of random data perturbation techniques. In: 3rd IEEE international conference on data mining, pp 99–106

  11. Dachman-Soled D, Malkin T, Raykova M, Yung M (2009) Efficient robust private set intersection. In: Applied cryptography and network security, volume 5536 of lecture notes in computer science. Springer, pp 125–142

  12. Chitti S, Xiong L, Liu L (2006) k nearest neighbor classification across multiple private databases. In: Proceedings of the 15th ACM international conference on information and knowledge management (CIKM), pp 840–841

  13. Aggarwal G, Mishra N, Pinkas B (2004) Secure computation of the kth ranked element. Adv Cryptol Eurocrypt 2004:40–55

    MATH  Google Scholar 

  14. Kissner L, Song D (2005) Privacy-preserving set operations. In: Proceedings of CRYPTO. Springer, pp 241–257

  15. Lindell Y, Pinkas B (2002) Privacy preserving data mining. J Cryptol 15:177–206

    Article  MathSciNet  Google Scholar 

  16. Kao B, Mamoulis N, Wong WK, Cheung DW (2009) Secure knn computation on encrypted databases. In: Proceedings of the 35th international conference on management of data, (SIGMOD). ACM, pp 139–152

  17. Upmanyu M, Namboodiri A, Srinathan K, Jawahar C (2010) Efficient privacy preserving k-means clustering. In: Intelligence and security informatics, vol 6122, LNCS. Springer Berlin/Heidelberg, pp 154–166

  18. Resende A, Railsback D, Dowsley R, Nascimento ACA, Aranha DF (2021) Fast privacy-preserving text classification based on secure multiparty computation. arXiv:2101.07365v2 [cs.CR]. 8 Jun 2021

  19. Yao AC (1986) How to generate and exchange secrets. In: 27th IEEE symposium on foundations of computer science, pp 162–167

  20. Qi Y, Atallah MJ (2008) Efficient privacy-preserving k-nearest neighbor search. In: Proceedings of 28th international conference on distributed computing (ICDCS). IEEE Computer Society, pp 311–319

  21. Wu W, Parampalli U, Liu J, Xian M (2019) Privacy preserving k-nearest neighbor classification over encrypted database in outsourced cloud environments, vol 22. World Wide Web, Springer, pp 101–123

  22. Singh MD, Krishna PR, Saxena A (2010) A cryptography based privacy preserving solution to mine cloud data. In: ACM compute, 2010

  23. Goldreich O (2004) The foundations of cryptography. Cambr Univ Press 2:28–34

    Google Scholar 

  24. Ishai Y, Kushilevitz E, Ostrovsky R (2005) Sufficient conditions for collision-resistant hashing. In: Theory of cryptography, vol. 3378, lecture notes in computer science. Springer, pp 445–456

  25. SP 800-78-3: cryptographic algorithms and key sizes for personal identification verification (piv). December 2010. http://csrc.nist.gov/publications/PubsSPs.html

  26. Wiener MJ (1990) Cryptanalysis of short RSA secret exponents. IEEE Trans Inf Theory 36(3):553–558

    Article  MathSciNet  Google Scholar 

  27. Lou D-C, Wu C-L, Chang T-J (2006) Computational complexity analyses of modular arithmetic for RSA cryptosystem. In: 23rd workshop on combinatorial mathematics and computation theory, 2006

  28. Lindell Y, Pinkas B (2002) Privacy preserving data mining. J Cryptol 15(3):177–206

    Article  MathSciNet  Google Scholar 

  29. Yang Z, Zhong S, Wright R (2005) Privacy-preserving classification of customer data without loss of accuracy. In: Proc. 5th SIAM international conference on data mining, pp 92–102, 2005

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Acknowledgements

The authors are thankful to the referees and the guest editor for their valuable comments, which resulted in an improved presentation of the paper. The authors are also thank R. Phani Bhushan, Scientist-G, Advanced Data Processing Research Institute (ADRIN) for providing system specific inputs to carryout this work.

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Authors and Affiliations

  1. CMR Technical Campus, Hyderabad, India

    Ashutosh Saxena

  2. Department of Computer Science and Engineering, National Institute of Technology Warangal (NITW), Warangal, India

    P. Radha Krishna

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  1. Ashutosh Saxena
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  2. P. Radha Krishna
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Correspondence to P. Radha Krishna.

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Saxena, A., Krishna, P.R. A novel cryptographic protocol for privacy preserving classification over distributed encrypted databases. J BANK FINANC TECHNOL 6, 31–41 (2022). https://doi.org/10.1007/s42786-022-00042-z

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  • DOI: https://doi.org/10.1007/s42786-022-00042-z

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