Mining Sequential Patterns from Outsourced Data via Encryption Switching

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

2 Citations (Scopus)


The increasing demand for data mining in business intelligence has led to a significant growth in the adoption of data mining as a service paradigm which enables companies to outsource their data and mining tasks to a cloud service provider. Despite the popularity of the paradigm, the companies hesitate to enable the cloud providers' access to their data considering customer privacy and intellectual property. In this paper, we propose a privacy-preserving two-party protocol which aims to mine direct sequential patterns from outsourced protected data. We focus on direct sequential pattern mining since it is a widely used primitive in business process analysis. Considering the accuracy and confidentiality, we choose encryption over statistical methods for data protection and processing. To be able to process the encrypted data, we adopt a homomorphic encryption scheme, ElGamal cryptosystem. The novelty of our scheme is that it introduces an encryption switching method that enables us to use both multiplicative and additive homomorphism on ElGamal cryptosystem. The results of our analyses show that our protocol is more efficient than the state-of-the-art proposals in terms of computational cost with a similar communication cost.
Original languageEnglish
Title of host publication16th Annual Conference on Privacy, Security and Trust, PST 2018
Place of PublicationPiscataway, NJ
Number of pages10
ISBN (Electronic)978-1-5386-7493-2
Publication statusPublished - 2018
EventPST 2018: 16th Annual Conference on Privacy, Security, and Trust - Belfast, United Kingdom
Duration: 28 Aug 201830 Aug 2018
Conference number: 16


ConferencePST 2018
Abbreviated titlePST 2018
CountryUnited Kingdom
Internet address


  • Applied Cryptography
  • data Mining as a Service
  • elGamal Cryptosystem
  • homomorphic Encryption


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