Abstract
Security measures, such as proving data integrity, became more important with the increase in popularity of cloud data storage services. Dynamic Provable Data Possession (DPDP) was proposed in the literature to enable the cloud server to prove to the client that her data is kept intact, even in a dynamic setting where the client may update her files. Realizing that variable-sized updates are very inefficient in DPDP (in the worst case leading to uploading the whole file again), Flexible DPDP (FlexDPDP) was proposed.
In this paper, we analyze FlexDPDP scheme and propose optimized algorithms. We show that the initial pre-processing phase at the client and server sides during the file upload (generally the most time-consuming operation) can be efficiently performed by parallelization techniques that result in a speed up of 6 with 8 cores. We propose a way of handling multiple updates at once both at the server and the client side, achieving an efficiency gain of 60 % at the server side and 90 % in terms of the client’s update verification time.
We deployed the optimized FlexDPDP on the large-scale network testbed PlanetLab and demonstrate the efficiency of our proposed optimizations on multi-client scenarios according to real workloads based on version control system traces.
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planetlab1.informatik.uni-wuerzburg.de.
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Acknowledgement
We would like to thank Ozan Okumuşoğlu at Koç University, Istanbul, Turkey for his contribution on testing and debugging, working on implementation of server-client side of the project and verification algorithms. We also acknowledge the support of TÜBİTAK (the Scientific and Technological Research Council of Turkey) under project numbers 111E019 and 112E115, Türk Telekom, Inc. under grant 11315-06, the European Union COST Actions IC1206 and IC1306, and Koç Sistem, Inc.
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Appendix: Optimization Algorithms
Appendix: Optimization Algorithms
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Esiner, E., Küpçü, A., Özkasap, Ö. (2015). Analysis and Optimization on FlexDPDP: A Practical Solution for Dynamic Provable Data Possession. In: Al-Saidi, A., Fleischer, R., Maamar, Z., Rana, O. (eds) Intelligent Cloud Computing. ICC 2014. Lecture Notes in Computer Science(), vol 8993. Springer, Cham. https://doi.org/10.1007/978-3-319-19848-4_5
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