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Cluster Computing

, Volume 21, Issue 2, pp 1189–1202 | Cite as

SearchaStore: fast and secure searchable cloud services

  • Wai-Kong LeeEmail author
  • Raphael C.-W. Phan
  • Geong-Sen Poh
  • Bok-Min Goi
Article

Abstract

The emergence of Cloud Computing is revolutionizing the way we store, query, analyze and consume data, which also bring forward other development that fundamentally changed our life style. For example, Industry 4.0 and Internet of Things (IoT) can improve the quality of manufacturing and many aspects in our daily life; both of them rely heavily on the cloud computing platform to develop. Central to this paradigm shift is the need to keep any common data, often held at remote outsourced locations and usually to be accessed by different authorized parties, secure from being leaked to unauthorized entities. When using the cloud services, consumer may want to encrypt sensitive data before uploading it to the cloud, but this will also eliminate the possibility to search the data efficiently in the cloud storage. A more practical solution to this is to employ a searchable encryption scheme in the cloud storage, so that user can query the encrypted data efficiently without revealing the sensitive data to the service provider. Besides the security and search features, performance of searchable encryption schemes is also very important when it comes to practical applications. In this paper, we propose several techniques to accelerate the search performance of encrypted data stored on the cloud. Notably, our techniques include massively parallel file encryption, multi-array keyword red black tree (KRBT) implementation, batched keyword search and enhanced parallel search in KRBT. To the best of our knowledge, SearchaStore is the first work that attempts to accelerate searchable encryption using GPU technology.

Keywords

Cloud service Secure outsourcing Keyword Red Black Tree Searchable symmetric encryption 

Notes

Acknowledgements

This work was supported partially by Universiti Tunku Abdul Rahman Research Fund (UTARRF) under Grant IPSR/RMC/UTARRF/2016-C1/G1.

References

  1. 1.
    Yang, G., Xie, L., Mantysalo, M., Zhou, X., Walter, S.K., Chen, Q., Zheng, L.: A healthcare information sharing scheme in distributed cloud networks. J. Clust. Comput. 18(4), 1405–1410 (2015)CrossRefGoogle Scholar
  2. 2.
    Tao, F., Zuo, Y., Xu, L.D., Zhang, L.: IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing. IEEE Trans. Ind. Inf. 10(2), 1547–1557 (2014)CrossRefGoogle Scholar
  3. 3.
    A. Mhlaba, M. Masinde.: Implementation of Middleware for Internet of Things in Asset Tracking Applications: In-lining Approach. IEEE International Conference on Industrial Informatics, INDIN, pp. 460-469, 2015Google Scholar
  4. 4.
    Mhlaba, A., Masinde, M.: Secure outsourcing of modular exponentiations in cloud and cluster computing. J. Clust. Comput. 19(2), 460–469 (2015)Google Scholar
  5. 5.
    Lee, S.G., Lee, D., Lee, S.: Personalized DTV program recommendation system under a cloud computing environment. IEEE Trans. Consum. Electron. 56(2), 1034–1042 (2010)CrossRefGoogle Scholar
  6. 6.
    Kim, Y., Ko, J., Shin, D., Kim, C., Park, C.: A frequency monitoring system development for wide-area power grid protection. J. Clust. Comput. 16(2), 209–219 (2013)CrossRefGoogle Scholar
  7. 7.
    Park, S., Park, E., Seo, J., Li, G.: Factors affecting the continuous use of cloud service-focused on security risks. J. Clust. Comput. 19(1), 485–495 (2015)CrossRefGoogle Scholar
  8. 8.
    Fang, S., Xu, L., Pei, H., Liu, Y.: An integrated approach to snowmelt flood forecasting in water resource management. IEEE Trans. Ind. Inf. 10(1), 548558 (2014)CrossRefGoogle Scholar
  9. 9.
    Xu, L.: Introduction: Systems science in industrial sectors. Syst. Res. Behav. Sci. 30(3), 211213 (2013)CrossRefGoogle Scholar
  10. 10.
    Song, X., Wagner, D., Perrig, A.: Practical techniques for searches on encrypted data. SP 00: Proceedings of the IEEE Symposium on Security and Privacy, pp. 44, (2000)Google Scholar
  11. 11.
    Goh, E.J.: Secure indexes. Cryptology ePrint Archive. Report 2003/216. http://eprint.iacr.org/2003/216/
  12. 12.
    Curtmola, R., Garay, J.A., Kamara, S., Ostrovsky, R.: Searchable Symmetric Encryption: Improved Definitions and Efficient Constructions. ACM Conference on Computer and Communications Security, CCS, pp. 7988. (2006)Google Scholar
  13. 13.
    Chase, M., Kamara, S.: Structured Encryption and Controlled Disclosure. ASIACRYPT, Lecture Notes in Computer Science. 6477, pp. 577594. Springer, Heidelberg(2010)Google Scholar
  14. 14.
    Kamara, S., Papamanthou, C., Roeder, T.: Dynamic searchable symmetric encryption. ACM Conference on Computer and Communications Security. pp. 965976. (2012)Google Scholar
  15. 15.
    Naveed, M., Prabhakaran, M., Gunter, C.A.: Dynamic searchable encryption via blind storage. Proceedings of the IEEE Symposium on Security and Privacy, pp. 639–654. (2014)Google Scholar
  16. 16.
    Curtmola, R., Garay, J.A., Kamara, S., Ostrovsky, R.: Searchable symmetric encryption: improved definitions and efficient constructions. J. Comput. Secur. 19(5), 895–934 (2011)CrossRefGoogle Scholar
  17. 17.
    Moataz, T., Justus, B., Ray, I., Cuppens-Boulahia, N., Cuppens, F., Ray, I.: Privacy-preserving multiple keyword search on outsourced data in the clouds. Lect. Notes Comput. Sci. 8566(2014), 66–81 (2014)CrossRefGoogle Scholar
  18. 18.
    Cash, D., Jarecki, S., Jutla, C.S., Krawczyk, H., Rosu, M., Steiner, M.: Highly-scalable searchable symmetric encryption with support for Boolean queries. Advances in Cryptology. Lecture Notes in Computer Science, vol. 8042, pp. 353–373. Springer, Berlin (2013)Google Scholar
  19. 19.
    Moataz, T., Shikfa, A.: Boolean symmetric searchable encryption. 8th ACM Symposium on Information, Computer and Communications Security, ASIA CCS, pp. 265276. (2013)Google Scholar
  20. 20.
    Yu, J., Lu, P., Zhu, Y., Xue, G., Li, M.: Toward secure multikeyword top-k retrieval over encrypted cloud data. IEEE Trans. Dependable Secur. Comput. 10(4), 239–250 (2013)CrossRefGoogle Scholar
  21. 21.
    Kamara, S., Papamanthou, C.: Parallel and Dynamic Searchable Symmetric Encryption. Financial Cryptography, pp. 258–274. Springer, Berlin (2013)Google Scholar
  22. 22.
    Cash, D., Jaeger, J., Jarecki, S., Jutla, C., Krawczyk, H., Rosu, M.C., Steiner, M.: Dynamic Searchable Encryption in Very-Large Databases: Data Structures and Implementation. Network and Distributed System Security Symposium, NDSS (2014)Google Scholar
  23. 23.
    Xia, Z., Wang, X., Sun, X., Wang, Q.: A secure and dynamic multi-keyword ranked search scheme over outsourced cloud data. IEEE Trans. Parallel Distrib.Syst. 27(2), 1–13 (2015)Google Scholar
  24. 24.
    Boneh, D., Kushilevitz, E., Ostrovsky, R., Skeith, W.E. III.: Public key encryption that allows PIR queries. CRYPTO, Lecture Notes in Computer Science. 4622, pp. 5067. Springer, Heidelberg. (2007)Google Scholar
  25. 25.
    Stefanov, E., Shi, E.: ObliviStore: High Performance Oblivious Cloud Storage. Proceedings of the IEEE Symposium on Security and Privacy, pp. 253–267. (2013)Google Scholar
  26. 26.
    Gentry, C., Halevi, S., Smart, N.P.: Fully homomorphic encryption with polylog overhead. Advances in Cryptology—EUROCRYPT, Lecture Notes in Computer Science, vol. 7237, pp. 465–482. Springer, Berlin (2012)Google Scholar
  27. 27.
    Hughes, D.M., Lim, I.S.: Kd-jump: a path-preserving stackless traversal for faster isosurface raytracing on GPUs. IEEE Trans. Vis. Comput. Graph. 15(6), 1555–1562 (2009)CrossRefGoogle Scholar
  28. 28.
    Kaczmarski, K.: B+-tree optimized for GPGPU. Lect. Notes Comput. Sci. 7566, 843–854 (2012)CrossRefGoogle Scholar
  29. 29.
    C. Kim, J., Chhugani, N., Satish, E., Sedlar, A., Nguyen, D., Kaldewey, T., Lee, V.W., Brandt, S.A., Dubey, P.: FAST: fast architecture sensitive tree search on modern CPUs and GPUs. Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, pp. 339–350. (2010)Google Scholar
  30. 30.
    Chen, X., Ren, L., Wang, Y., Yang, H.: GPU-accelerated sparse LU factorization for circuit simulation with performance modeling. IEEE Trans. Parallel Distrib. Syst. 26(3), 786–795 (2015)CrossRefGoogle Scholar
  31. 31.
    Mei, S., He, M., Shen, Z.: Optimizing Hopfield Neural Network for Spectral Mixture Unmixing on GPU Platform. IEEE Geosci. Remote Sens. Lett. 11(4), 818–822 (2014)CrossRefGoogle Scholar
  32. 32.
    Hu, L., Nooshabadi, S., Mladenov, T.: Forward error correction with Raptor GF(2) and GF(256) codes on GPU. IEEE Trans. Consum. Electron. 59(1), 273–280 (2013)CrossRefGoogle Scholar
  33. 33.
    Lee, W.K., Cheong, H.S., Phan, Raphael C.-W., Goi, B.M.: Fast implementation of block ciphers and PRNGs in Maxwell GPU architecture. J. Clust. Comput. 19(1), 335–347 (2016)CrossRefGoogle Scholar
  34. 34.
    Yang, Y., Guan, Z., Sun, H., Chen, Z.: Accelerating RSA with fine-grained parallelism using GPU. Information Security Practice and Experience, Lecture Notes in Computer Science, vol 9065, pp. 454-468. (2015)Google Scholar
  35. 35.
    Park, H., Park, K.: Parallel algorithms for redblack trees. Theor. Comput. Sci. 262(12), 415435 (2001)Google Scholar
  36. 36.
    Enron Dataset. https://www.cs.cmu.edu/enron/. (2015)

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Faculty of Information and Communication TechnologyUniversiti Tunku Abdul RahmanKamparMalaysia
  2. 2.Faculty of EngineeringMultimedia UniversityCyberjayaMalaysia
  3. 3.MIMOS BerhadKuala LumpurMalaysia
  4. 4.Lee Kong Chian Faculty of Engineering and ScienceUniversiti Tunku Abdul RahmanSungai LongMalaysia

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