Data Sets

There are different datasets available to conduct the simulations and evaluate the time complexity of the algorithms involved in the searchable encryption scheme.

Enron email dataset: The Enron email dataset carries approximately 500,000 emails generated by employees of the Enron organization [10]. It was acquired by the Federal Energy Regulatory commission throughout its research of Enron’s collapse. This dataset was gathered and organized under the Cognitive Assistant that Learns and Organizes (CALO) project. It carries statistics from approximately 150 users, in most cases senior management of Enron. The data is organized into folders. These records were made public and published to the Internet by the Federal Energy Regulatory Commission in the course of its research. Later this dataset was purchased by Leslie Kaelbling at MIT, who identified a number of integrity problems which were then corrected and the data set made available for research projects. The invalid email addresses were transformed to a general form like This email address is being protected from spam bots, you need Javascript enabled to view it (when the recipient was specified) or to This email address is being protected from spam bots, you need Javascript enabled to view it when no recipient was specified.

Internet request for comments (RFC): This dataset has 6,870 plaintext files with a total size about 349MB [11]. To extract the keywords from each RFC file, a hermetic word frequency counter [12] is generally used.

Inter-domain Applications

Searchable encryption is not just restricted to the cryptography domain but can be applied to and combined with different platforms. Apart from the application of searchable encryption on the cloud platform there are other application scenarios that have been seen in the literature. Researchers have applied and tested this concept for different platforms like IoT (Internet of things), wireless sensor networks (WSN), fog computing and the combination of these [13, 14, 15]. Most recently, blockchain technology has been leveraged to build more robust searchable encryption schemes. Most of the developments in this direction has been seen from 2017 onwards.

A new area where searchable encryption application has been seen is the cloud-assisted wireless sensor networks (CWSN). Xu [16] proposed a lightweight searchable encryption scheme for CWSN with a reduced cost of generating ciphertext and performing search. The owner of the sensor network generates public and secret key pairs and stores the public components at all the sensor nodes. The sensor nodes collect and store the encrypted data on the cloud server. To retrieve the data, the data owner generates a trapdoor for the keyword and sends it to the cloud server to perform the search.

IoT devices contribute the majority of data in today’s digital world and the collected data is stored in cloud servers. The collected data may be sensitive; therefore, searchable encryption can have applications for cloud-based IoT. Wu et al. [17] proposed a searchable encryption scheme for the same. The working of their system is the same as the standard searchable encryption in a public-key setting. Apart from the basic functionality, they focused on developing the KGA resistant scheme.

Miao et al. [18] applied fine-grained conjunctive keyword search to fog computing. It is basically an extension of cloud computing where fog nodes are placed between the end users and the cloud. Their scheme is lightweight in the sense that most of the computationally intensive tasks are delegated to the fog nodes. The end users only perform the partial computations; the final ciphertext and even the final trapdoor are generated by the fog nodes and sent to the cloud server. The fog nodes receive the search result returned by the cloud server and perform partial decryption to further reduce the burden on end users. The cloud server has the role of searching like always and instead of returning the search result to the end user, now it returns it to the intermediate fog nodes that perform all necessary computations. They also take into account the process of updating the attributes in the access policy, hence supporting dynamic policy. The data from end users are collected by the IoT devices (wearable or mobile devices) used by these users.

During this timeframe, when research was going on to test the applicability of searchable encryption in different scenarios as discussed above, there came a major milestone in the journey of searchable encryption schemes. This milestone was achieved by using the most innovative technology of the century: blockchain technology. We have seen a huge revolution initiated by this technology in different sectors such as finance, healthcare, voting and a lot more. Hu et al. [19] proposed an entirely new platform for the searchable encryption. They leveraged the power of smart contracts to develop a secure searchable encryption in a symmetric-key setting. The resulting scheme was quite robust as we now need not worry about situations like a malicious cloud server or whether the cloud server is dishonest. The inherent design of the blockchain technology take cares of these issues. Instead of using the cloud server, the data owner stores his/her data in an encrypted form over the public blockchain platform, Ethereum. The scheme was implemented and tested to check the applicability in practical scenarios. Most recently in June, 2020, the application of blockchain technology has been seen in developing searchable encryption in a public-key setting by Liu et al. [17]. They have not replaced the cloud server with the blockchain, but have leveraged blockchain technology to reduce computational burden and to build more realistic SE schemes. They have used consortium blockchain to initialize the system, generate the final trapdoor and handle the event of user revocation. The cloud as usual stores the encrypted data and performs partial decryption on the behalf of users. The blockchain technology here makes the SE scheme decentralized by completely eliminating the need of a central authority and has also reduced the computational burden from data users.

Summary

This chapter discussed different open-source tools for the design and development of secure, searchable encryption schemes. It also outlined some of the best practices and guidelines for ensuring the security of searchable encryption schemes. Further, this chapter discussed different areas of application for searchable encryption and how this technique plays an important role in different domains.

References

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  • 2. D. Boneh, B. Lynn, and H. Shacham, “Short signatures from the Weil pairing,” in International Conference on the Theory and Application of Cryptology and Information Security, 2001, pp. 514-532.
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  • 10. W. W. Cohen, “Enron email dataset webpage.” Carnegie Mellon University, http://www.cs.cmu.edu/~enron, 2009
  • 11. “RFC: Request for comments database,” https://www.ietf.org/rfc/
  • 12. “HERMETIC: Hermetic word frequency counter,” http://www.hermetic.ch/wfc/ wfc.htm
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  • 16. P. Xu, S. He, W. Wang, W. Susilo, and H. Jin, “Lightweight searchable public- key encryption for cloud-assisted wireless sensor networks,” IEEE Trans. Ind. Informatics, vol. 14, no. 8, pp. 3712-3723, 2017.
  • 17. L. Wu, B. Chen, K.-K. R. Choo, and D. He, “Efficient and secure searchable encryption protocol for cloud-based internet of things,” J. Parallel Distrib. Comput, vol. Ill, pp. 152-161, 2018.
  • 18. Y. Miao, J. Ma, X. Liu, J. Weng, H. Li, and H. Li, “Lightweight fine-grained search over encrypted data in fog computing,” IEEE Trans. Serv. Comput., 2018.
  • 19. S. Hu, C. Cai, Q. Wang, C. Wang, X. Luo, and K. Ren, “Searching an Encrypted Cloud Meets Blockchain: A Decentralized, Reliable and Fair Realization,” in IEEE INFOCOM 2018-IEEE Conference on Computer Communications, 2018, pp. 792-800.
  • 20. S. Liu, J. Yu, Y. Xiao, Z. Wan, S. Wang, and B. Yan, “BC-SABE: Blockchain- aided searchable attribute-based encryption for Cloud-IoT,” IEEE Internet Things ]., 2020.
 
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