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SEMFS: Secure and Efficient Multi-keyword Fuzzy Search for Cloud Storage

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Information Systems Security (ICISS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10717))

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Abstract

Cloud computing has become a popular technology for outsourcing data and providing reliable data services. Encryption is essential to preserve privacy of the outsourced sensitive data. Keyword search over encrypted data would enhance the effective utilization of outsourced storage. In this work, we propose an efficient Searchable Symmetric Encryption (SSE) scheme called SEMFS (Secure & Efficient Multi-keyword Fuzzy Search Scheme) to allow the cloud to search over outsourced encrypted data. SEMFS uses quotient filter for efficient indexing and faster searching. The most attractive feature of this scheme is to allow update the entries of index file dynamically (to achieve better performance) preserving data privacy. Experimental analysis shows that SEMFS achieves higher throughput than the bloom filter based scheme, when implemented. Security of SEMFS has been analyzed against known ciphertext and known plaintext attack models.

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Notes

  1. 1.

    Let, \(\alpha \times \beta =\gamma \) where \(\alpha \) is the total number of slots in QF and \(\beta \) is the total number of different values that can be stored in each slot.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna, India

    Sanjeet Kumar Nayak & Somanath Tripathy

Authors
  1. Sanjeet Kumar Nayak
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  2. Somanath Tripathy
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Corresponding author

Correspondence to Sanjeet Kumar Nayak .

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Bombay, Mumbai, India

    Rudrapatna K. Shyamasundar

  2. Indian Institute of Technology Bombay, Mumbai, India

    Virendra Singh

  3. Rutgers University, Newark, New Jersey, USA

    Jaideep Vaidya

Appendix A Bloom Filter

Appendix A Bloom Filter

Bloom filter is a space-efficient data structure for representing a set in order to support membership queries [5]. A bloom filter for representing a set S containing n number of elements is described by an array of m bits size. All these m bits are initialized to 0. It uses k independent hash functions defined as \(\mathcal {H} = \{h_i|h_i : S \rightarrow [1, m], 1\le i \le k\}\). To insert an element \(s \in S\) into the bloom filter, all the \(h_i(s)\)-th position are set to 1 in the m bit array. To search an element \(q \in S\), first we find k number of array positions using \(h_i(q), 1\le i \le k\). Then, we check if the corresponding bit of any of the k position is 0, then q is definitely not present in the set S. Otherwise, q probably present in the set S. Consider the following example. Figure 6 shows an example of bloom filter with m = 10 and k = 3. Here, the filter represents the set \(S=\{A, B\}\). Black colored arrow shows the corresponding positions of the bloom filter that each element of set S is mapped to. Now if we will search for an element D, then we can observe from the figure that at least one position to which D is mapped contains 0 (shown in red colored lines). So D is definitely not present in the set. If we will search for an element C, then we can observe from the figure that all the positions to which C is mapped contain 1. Hence, C is probably present in the set.

Fig. 6.
figure 6

An example of boolean filter (Color figure online)

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Nayak, S.K., Tripathy, S. (2017). SEMFS: Secure and Efficient Multi-keyword Fuzzy Search for Cloud Storage. In: Shyamasundar, R., Singh, V., Vaidya, J. (eds) Information Systems Security. ICISS 2017. Lecture Notes in Computer Science(), vol 10717. Springer, Cham. https://doi.org/10.1007/978-3-319-72598-7_4

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  • DOI: https://doi.org/10.1007/978-3-319-72598-7_4

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