Skip to main content
SpringerLink
Log in

Effectively and Efficiently Supporting Encrypted OLAP Queries over Big Data: Models, Issues, Challenges

  • Conference paper
  • First Online:
Proceedings of the 7th International Conference on Emerging Databases

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 461))

  • 862 Accesses

Abstract

Due to emerging technologies like Clouds, recently the problem of encrypting and querying big data is of great interest trough the community. Here, the main problem consists in devising effective and efficient encryption schemes for big data, and then effective and efficient query algorithms for querying such data in their encrypted form directly. By comparing both lines of research, it emerges that querying encrypted big data plays the major role, as the encryption phase is usually conducted on top of well-recognized state-of-the-art encryption schemes. On the other hand, OLAP data are a knowledge-rich class of big data that are extremely important for latest big data analytics tools. Inspired by these two authoritative research trends, in this paper we provide the following contributions: (i) an overview of most relevant initiatives in the scientific field of querying encrypted OLAP data; (ii) critical discussion on open issues and research challenges that will dominate the future scene of the investigated research topic.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cuzzocrea, A.: Privacy and security of big data: current challenges and future research perspectives. In: Proceedings of the First International Workshop on Privacy and Secuirty of Big Data, PSBD@CIKM 2014, pp. 45–47, Shanghai, China, 7 November 2014

    Google Scholar 

  2. Cuzzocrea, A., Russo, V.: Privacy preserving OLAP and OLAP security. In: Encyclopedia of Data Warehousing and Mining, 2nd edn. (4 Volumes), pp. 1575–1581 (2009)

    Google Scholar 

  3. Bertino, E.: Big data security and privacy. In: 2016 IEEE International Conference on Big Data, BigData 2016, p. 3, Washington DC, USA, 5–8 December 2016

    Google Scholar 

  4. Nelson, B., Olovsson, T.: Security and privacy for big data: a systematic literature review. In: 2016 IEEE International Conference on Big Data, BigData 2016, pp. 3693–3702, Washington DC, USA, 5–8 December 2016

    Google Scholar 

  5. Moreno, J., Serrano, M.A., Fernández-Medina, E.: Main issues in big data security. Future Internet 8(3), 44 (2016)

    Article  Google Scholar 

  6. Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., Pirahesh, H.: Data cube: a relational aggregation operator generalizing group-by, cross-tab, and sub totals. Data Min. Knowl. Discov. 1(1), 29–53 (1997)

    Article  Google Scholar 

  7. Li, J., Ma, R., Guan, H.: TEES: an efficient search scheme over encrypted data on mobile cloud. IEEE Trans. Cloud Comput. 5(1), 126–139 (2017)

    Article  Google Scholar 

  8. Lan, C., Li, H., Yin, S., Teng, L.: A new security cloud storage data encryption scheme based on identity proxy re-encryption. I. J. Netw. Secur. 19(5), 804–810 (2017)

    Google Scholar 

  9. Cui, H., Yuan, X., Wang, C.: Harnessing encrypted data in cloud for secure and efficient mobile image sharing. IEEE Trans. Mob. Comput. 16(5), 1315–1329 (2017)

    Article  Google Scholar 

  10. Arasu, A., Eguro, K., Kaushik, R., Ramamurthy, R.: Querying encrypted data. In: International Conference on Management of Data, SIGMOD 2014, pp. 1259–1261, Snowbird, UT, USA, 22–27 June 2014

    Google Scholar 

  11. Cuzzocrea, A., Song, I., Davis, K.C.: Analytics over large-scale multidimensional data: the big data revolution! In: ACM 14th International Workshop on Data Warehousing and OLAP, Proceedings, DOLAP 2011, pp. 101–104, Glasgow, UK, 28 October 2011

    Google Scholar 

  12. Cuzzocrea, A.: Analytics over big data: exploring the convergence of datawarehousing, OLAP and data-intensive cloud infrastructures. In: 37th Annual IEEE Computer Software and Applications Conference, COMPSAC 2013, pp. 481–483, Kyoto, Japan, 22–26 July 2013

    Google Scholar 

  13. Sakr, S., Elgammal, A.: Towards a comprehensive data analytics framework for smart healthcare services. Big Data Res. 4, 44–58 (2016)

    Article  Google Scholar 

  14. Cuzzocrea, A., Grasso, G.M.: Querying encrypted OLAP data. In: 41st Annual IEEE Computer Software and Applications Conference, COMPSAC 2017, Turin, Italy, 4–8 July 2017

    Google Scholar 

  15. Lopes, C.C., Times, V.C., Matwin, S., Ciferri, R.R., de Aguiar Ciferri, C.D.: Processing OLAP queries over an encrypted data warehouse stored in the cloud. In: Data Warehousing and Knowledge Discovery - 16th International Conference, DaWaK 2014, Proceedings, Munich, Germany, pp. 195–207, 2–4 September 2014

    Google Scholar 

  16. Guermazi, E., Ayed, M.B., Ben-Abdallah, H.: Adaptive security for cloud data warehouse as a service. In: 14th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2015, pp. 647–650, Las Vegas, NV, USA, 28 June–1 July 2015

    Google Scholar 

  17. Lopes, C.C., Times, V.C., Matwin, S.: Towards cloud data warehouses of multivalued encrypted values. JIDM 5(3), 335–348 (2014)

    Google Scholar 

  18. Lopes, C.C., Times, V.C.: A framework for investigating the performance of sum aggregations over encrypted data warehouses. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing, pp. 1000–1007, Salamanca, Spain, 13–17 April 2015

    Google Scholar 

  19. Gentry, C.: Fully homomorphic encryption using ideal lattices. In: Proceedings of the 41st Annual ACM Symposium on Theory of Computing, STOC 2009, pp. 169–178, Bethesda, MD, USA, 31 May–2 June 2009

    Google Scholar 

  20. Chen, K., Kavuluru, R., Guo, S.: RASP: efficient multidimensional range query on attack-resilient encrypted databases. In: First ACM Conference on Data and Application Security and Privacy, CODASPY 2011, Proceedings, pp. 249–260, San Antonio, TX, USA, 21–23 February 2011

    Google Scholar 

  21. Wen, M., Lu, R., Lei, J., Liang, X., Li, H., Shen, X.: ECQ: an efficient conjunctive query scheme over encrypted multidimensional data in smart grid. In: 2013 IEEE Global Communications Conference, GLOBECOM 2013, pp. 796–801, Atlanta, GA, USA, 9–13 December 2013

    Google Scholar 

  22. Tu, S., Kaashoek, M.F., Madden, S., Zeldovich, N.: Processing analytical queries over encrypted data. PVLDB 6(5), 289–300 (2013)

    Google Scholar 

  23. Eavis, T., Taleb, A.: Mapgraph: efficient methods for complex olap hierarchies. In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, CIKM 2007, pp. 465–474, Lisbon, Portugal, 6–10 November 2007

    Google Scholar 

  24. Cuzzocrea, A.: Aggregation and multidimensional analysis of big data for large-scale scientific applications: models, issues, analytics, and beyond. In: Proceedings of the 27th International Conference on Scientific and Statistical Database Management, SSDBM 2015, pp. 23:1–23:6, La Jolla, CA, USA, 29 June–1 July 2015

    Google Scholar 

  25. Bothe, S., Cuzzocrea, A., Karras, P., Vlachou, A.: Skyline query processing over encrypted data: an attribute-order-preserving-free approach. In: Proceedings of the First International Workshop on Privacy and Secuirty of Big Data, PSBD@CIKM 2014, pp. 37–43, Shanghai, China, 7 November 2014

    Google Scholar 

  26. Fang, M., Shivakumar, N., Garcia-Molina, H., Motwani, R., Ullman, J.D.: Computing iceberg queries efficiently. In: Proceedings of 24th International Conference on Very Large Data Bases, VLDB 1998, pp. 299–310, 24–27 August 1998, New York City, New York, USA (1998)

    Google Scholar 

  27. Börzsönyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: Proceedings of the 17th International Conference on Data Engineering, pp. 421–430, 2–6 April 2001, Heidelberg, Germany (2001)

    Google Scholar 

  28. Cuzzocrea, A., Furfaro, F., Saccà, D.: Enabling OLAP in mobile environments via intelligent data cube compression techniques. J. Intell. Inf. Syst. 33(2), 95–143 (2009)

    Article  Google Scholar 

  29. Cuzzocrea, A.: Accuracy control in compressed multidimensional data cubes for quality of answer-based OLAP tools. In: 18th International Conference on Scientific and Statistical Database Management, SSDBM 2006, pp. 301–310, 3–5 July 2006, Vienna, Austria, Proceedings (2006)

    Google Scholar 

  30. Cuzzocrea, A., Matrangolo, U.: Analytical synopses for approximate query answering in OLAP environments. In: 15th International Conference on Database and Expert Systems Applications, DEXA 2004, Proceedings, pp. 359–370, Zaragoza, Spain, 30 August–3 September 2004

    Google Scholar 

  31. Cuzzocrea, A., Saccà, D., Ullman, J.D.: Big data: a research agenda. In: 17th International Database Engineering & Applications Symposium, IDEAS 2013, pp. 198–203, Barcelona, Spain, 9–11 October 2013

    Google Scholar 

  32. Abadi, D.J., Boncz, P.A., Harizopoulos, S.: Column oriented database systems. PVLDB 2(2), 1664–1665 (2009)

    Google Scholar 

  33. Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The hadoop distributed file system. In: IEEE 26th Symposium on Mass Storage Systems and Technologies, MSST 2012, pp. 1–10, Lake Tahoe, Nevada, USA, 3–7 May 2010

    Google Scholar 

  34. Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. DIA Department, University of Trieste and ICAR-CNR, Trieste, Italy

    Alfredo Cuzzocrea

Authors
  1. Alfredo Cuzzocrea
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alfredo Cuzzocrea .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Inha University, Incheon, Korea (Republic of)

    Wookey Lee

  2. Department of Information and Communication Engineering, Inha University, Incheon, Korea (Republic of)

    Wonik Choi

  3. Department of Computer Science and Engineering, Sogang University, Seoul, Korea (Republic of)

    Sungwon Jung

  4. Department of Library and Information Science, Yonsei University, Seoul, Korea (Republic of)

    Min Song

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Cuzzocrea, A. (2018). Effectively and Efficiently Supporting Encrypted OLAP Queries over Big Data: Models, Issues, Challenges. In: Lee, W., Choi, W., Jung, S., Song, M. (eds) Proceedings of the 7th International Conference on Emerging Databases. Lecture Notes in Electrical Engineering, vol 461. Springer, Singapore. https://doi.org/10.1007/978-981-10-6520-0_36

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-6520-0_36

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6519-4

  • Online ISBN: 978-981-10-6520-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation