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Privacy Preserving Elastic Stream Processing with Clouds Using Homomorphic Encryption

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Book cover Database Systems for Advanced Applications (DASFAA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11447))

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Abstract

Prevalence of the Infrastructure as a Service (IaaS) clouds has enabled organizations to elastically scale their stream processing applications to public clouds. However, current approaches for elastic stream processing do not consider the potential security vulnerabilities in cloud environments. In this paper we describe the design and implementation of an Elastic Switching Mechanism for data stream processing which is based on Homomorphic Encryption (HomoESM). The HomoESM not only does elastically scale data stream processing applications into public clouds but also preserves the privacy of such applications. Using a real world test setup, which includes an email filter benchmark and a web server access log processor benchmark (EDGAR) we demonstrate the effectiveness of our approach. Multiple experiments on Amazon EC2 indicate that the proposed approach for Homomorphic encryption provides significant results which is 10% to 17% improvement of average latency in the case of email filter benchmark and EDGAR benchmarks respectively. Furthermore, EDGAR add/subtract operations and comparison operations showed 6.13% and 26.17% average latency improvements respectively. These promising results pave the way for real world deployments of privacy preserving elastic stream processing in the cloud.

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Notes

  1. 1.

    https://github.com/arosharodrigo/event-publisher.

  2. 2.

    https://github.com/arosharodrigo/statistics-collector.

  3. 3.

    https://github.com/arosharodrigo/simple-siddhi-server.

References

  1. Blount, M., et al.: Real-time analysis for intensive care: development and deployment of the artemis analytic system. IEEE Eng. Med. Biol. Mag. 29(2), 110–118 (2010)

    Article  Google Scholar 

  2. Cervino, J., Kalyvianaki, E., Salvachua, J., Pietzuch, P.: Adaptive provisioning of stream processing systems in the cloud. In: 2012 IEEE 28th International Conference on Data Engineering Workshops (ICDEW), pp. 295–301, April 2012

    Google Scholar 

  3. Cuzzocrea, A., Chakravarthy, S.: Event-based lossy compression for effective and efficient OLAP over data streams. Data Knowl. Eng. 69(7), 678–708 (2010)

    Article  Google Scholar 

  4. Dai, W., Sunar, B.: cuHE: a homomorphic encryption accelerator library. Cryptology ePrint Archive, Report 2015/818 (2015). https://eprint.iacr.org/2015/818

  5. Dayarathna, M., Perera, S.: Recent advancements in event processing. ACM Comput. Surv. 51(2), 33:1–33:36 (2018)

    Article  Google Scholar 

  6. Dayarathna, M., Suzumura, T.: A mechanism for stream program performance recovery in resource limited compute clusters. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds.) DASFAA 2013. LNCS, vol. 7826, pp. 164–178. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37450-0_12

    Chapter  Google Scholar 

  7. Dayarathna, M., Suzumura, T.: A performance analysis of system S, S4, and esper via two level benchmarking. In: Joshi, K., Siegle, M., Stoelinga, M., D’Argenio, P.R. (eds.) QEST 2013. LNCS, vol. 8054, pp. 225–240. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40196-1_19

    Chapter  Google Scholar 

  8. DERA. Edgar log file data set (2017). https://www.sec.gov/dera/data/edgar-log-file-data-set.html

  9. Gentry, C.: Fully homomorphic encryption using ideal lattices. In: Proceedings of the Forty-First Annual ACM Symposium on Theory of Computing, STOC 2009, pp. 169–178. ACM, New York (2009)

    Google Scholar 

  10. Google: Cloud dataflow (2017). https://cloud.google.com/dataflow/

  11. Halevi, S.: An implementation of homomorphic encryption (2017). https://github.com/shaih/HElib

  12. Halevi, S., Shoup, V.: Algorithms in HElib. In: Garay, J.A., Gennaro, R. (eds.) CRYPTO 2014. LNCS, vol. 8616, pp. 554–571. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44371-2_31

    Chapter  MATH  Google Scholar 

  13. Hummer, W., Satzger, B., Dustdar, S.: Elastic stream processing in the cloud. Wiley Interdisc. Rev. Data Min. Knowl. Discovery 3(5), 333–345 (2013)

    Article  Google Scholar 

  14. IBM: Streaming analytics (2017). https://www.ibm.com/cloud/streaming-analytics

  15. Ishii, A., Suzumura, T.: Elastic stream computing with clouds. In: 2011 IEEE 4th International Conference on Cloud Computing, pp. 195–202, July 2011

    Google Scholar 

  16. Jayasekara, S., Perera, S., Dayarathna, M., Suhothayan, S.: Continuous analytics on geospatial data streams with WSO2 complex event processor. In: Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems, DEBS 2015, pp. 277–284. ACM, New York (2015)

    Google Scholar 

  17. Klimt, B., Yang, Y.: Introducing the enron corpus, p. 2, January 2004

    Google Scholar 

  18. Loesing, S., Hentschel, M., Kraska, T., Kossmann, D.: Stormy: an elastic and highly available streaming service in the cloud. In: Proceedings of the 2012 Joint EDBT/ICDT Workshops, EDBT-ICDT 2012, pp. 55–60. ACM, New York (2012)

    Google Scholar 

  19. Page, A., Kocabas, O., Ames, S., Venkitasubramaniam, M., Soyata, T.: Cloud-based secure health monitoring: optimizing fully-homomorphic encryption for streaming algorithms. In: 2014 IEEE Globecom Workshops (GC Wkshps), pp. 48–52, December 2014

    Google Scholar 

  20. Quoc, D.L., Chen, R., Bhatotia, P., Fetzer, C., Hilt, V., Strufe, T.: Streamapprox: approximate computing for stream analytics. In: Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference, Middleware 2017, pp. 185–197. ACM, New York (2017)

    Google Scholar 

  21. Ravindra, S., Dayarathna, M., Jayasena, S.: Latency aware elastic switching-based stream processing over compressed data streams. In: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering, ICPE 2017, pp. 91–102. ACM, New York (2017)

    Google Scholar 

  22. Shaon, F., Kantarcioglu, M., Lin, Z., Khan, L.: SGX-BigMatrix: a practical encrypted data analytic framework with trusted processors. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS 2017, pp. 1211–1228. ACM, New York (2017)

    Google Scholar 

  23. Striim: Striim delivers streaming hybrid cloud integration to microsoft azure (2017). http://www.striim.com/press/hybrid-cloud-integration-to-microsoft-azure

  24. Theeten, B., Bedini, I., Cogan, P., Sala, A., Cucinotta, T.: Towards the optimization of a parallel streaming engine for telco applications. Bell Labs Tech. J. 18(4), 181–197 (2014)

    Article  Google Scholar 

  25. Togan, M., Plesca, C.: Comparison-based computations over fully homomorphic encrypted data. In: 2014 10th International Conference on Communications (COMM), pp. 1–6, May 2014

    Google Scholar 

  26. WSO2: WSO2 stream processor (2018). https://wso2.com/analytics-and-stream-processing

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Moratuwa, Moratuwa, Sri Lanka

    Arosha Rodrigo & Sanath Jayasena

  2. WSO2, Inc., Mountain View, USA

    Miyuru Dayarathna

Authors
  1. Arosha Rodrigo
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  2. Miyuru Dayarathna
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  3. Sanath Jayasena
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Corresponding author

Correspondence to Miyuru Dayarathna .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Guoliang Li

  2. Duke University, Durham, NC, USA

    Jun Yang

  3. University of Porto, Porto, Portugal

    Joao Gama

  4. Chiang Mai University, Chiang Mai, Thailand

    Juggapong Natwichai

  5. Beihang University, Beijing, China

    Yongxin Tong

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Rodrigo, A., Dayarathna, M., Jayasena, S. (2019). Privacy Preserving Elastic Stream Processing with Clouds Using Homomorphic Encryption. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11447. Springer, Cham. https://doi.org/10.1007/978-3-030-18579-4_16

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  • DOI: https://doi.org/10.1007/978-3-030-18579-4_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-18578-7

  • Online ISBN: 978-3-030-18579-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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