[DPM 2015] PerfectDedup - Secure Data Deduplication for Cloud Storage
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With the continuous increase of cloud storage adopters, data deduplication has become a necessity for cloud providers. By storing a unique copy of duplicate data, cloud providers greatly reduce their storage and data transfer costs. Unfortunately, deduplication introduces a number of new security challenges. We propose PerfectDedup, a novel scheme for secure data deduplication, which takes into account the popularity of the data segments and leverages the properties of Perfect Hashing in order to assure block-level deduplication and data con dentiality at the same time. We show that the client-side overhead is minimal and the main computational load is outsourced to the cloud storage provider.
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[DPM 2015] PerfectDedup - Secure Data Deduplication for Cloud Storage
- 1. PerfectDedup Secure Data Deduplication Pasquale PUZIO pasquale@secludit.com SecludIT & EURECOM Refik Molva (EURECOM) Melek Önen (EURECOM) Sergio Loureiro (SecludIT) 10th DPM International Workshop on Data Privacy Management Vienna, Austria, September 21st 2015
- 2. Agenda • Problem Statement – Data Deduplication for Cloud Storage – Convergent Encryption • Our solution – Data Popularity – Perfect Hashing – PerfectDedup: Secure Popularity Detection – Security – Performance Evaluation 2
- 3. Deduplication • Storing duplicate data only once • Cross-user + Client-side + Block-level 3
- 4. Deduplication vs Encryption … but it does not work on encrypted data! D = Hello World D = Hello World ENCRYPTION with K1 ENCRYPTION with K2 owhfgr0wgr[w hfrw0[h0[ergh e0[gh0[eg dfjl;dbfrwbfirbf roepthwobgfr ugtwertgrtwu 4
- 5. Convergent Encryption • Data Encryption key derived from Data K = hash(Data) • Deterministic & Symmetric Encryption D = Hello World D = Hello World ENCRYPTION with H(D) ENCRYPTION with H(D) klfgwilegfiorw egtriegtiergiei ergriegrigfifiw klfgwilegfiorw egtriegtiergiei ergriegrigfifiw 5 Douceur, John R., et al. "Reclaiming space from duplicate files in a serverless distributed file system." Distributed Computing Systems, 2002. Proceedings. 22nd International Conference on. IEEE, 2002.
- 6. Convergent Encryption MISSING INFORMATION How to achieve safe Convergent Encryption in the Cloud ? 6 Drew Perttula, Brian Warner, and Zooko Wilcox-O'Hearn, 2008-03-20 https://tahoe-lafs.org/hacktahoelafs/drew_perttula.html
- 7. Data Popularity • Different protection based on data-segment popularity • Popular data Not confidential To be deduplicated Convergent Encryption • Unpopular data Confidential To be protected Semantically-Secure Encryption 7 Stanek, Jan, et al. "A secure data deduplication scheme for cloud storage." Financial Cryptography and Data Security. Springer Berlin Heidelberg, 2014. 99-118.
- 8. How to securely detect popularity ? CSP . . . B . . . Is block B popular ? YES / NO • Block B must not be disclosed if it is unpopular (sensitive) CLIENT 8
- 9. PHF-based Lookup 9 ID Belazzougui, Djamal, Fabiano C. Botelho, and Martin Dietzfelbinger. "Hash, displace, and compress." Algorithms-ESA 2009. Springer Berlin Heidelberg, 2009. 682-693.
- 10. PerfectDedup • Based on «Secure» Perfect Hashing – One-wayness • Popular block IDs Collision-free hash function (PHF) • BENEFITS: – Efficient (linear) generation of a new PHF (outsourced to the Cloud) – Compact representation of PHF – Very efficient (constant) evaluation on a block ID 10
- 11. Security UNPOPULAR P POPULAR P CSP . . . . . . PHF(ID) = i i ID Block is popular 1-to-1 mapping No confidentiality issue 11
- 12. Security UNPOPULAR P POPULAR P CSP . . . . . . PHF(ID) = i i ID’ Block is unpopular Collisions are well-distributed One-wayness property 12
- 13. PerfectDedup CSP . . . B . . . Is block B popular ? YES / NO INDEX SERVICE If NO POPULARITY TRANSITION ? YES / NO CLIENT 13
- 15. Performance Evaluation 0 1 2 3 4 5 6 7 8 9 10 UNPOPULAR FILE POPULARITY TRANSITION POPULAR FILE Time(inseconds) Scenario Client File Split Client Convergent Encryption Client Popularity Check Client Symmetric Encryption Idx Service Update Cloud Generate PHF Cloud Store Hash Table Cloud Popularity Check Cloud Upload Processing 15
- 16. Conclusions • Popularity-based Deduplication • Secure Perfect Hashing • Secure & Lightweight for the client • Costly tasks outsourced to the Cloud • Low overhead 16
- 17. Future Work • Optimization of PHF generation • Deployment in real production environments 17
- 18. THANK YOU Questions ? Don’t be shy ! pasquale@secludit.com
Editor's Notes
- Hello everyone, my name’s Pasquale Puzio. I’m a PhD student at EURECOM & SecludIT under the supervision of Refik MOLVA and Sergio LOUREIRO. Today I’m gonna talk about PerfectDedup, which is our last work on secure data deduplication Data Deduplication + Confidentiality
- Let’s talk quickly about the agenda. Today I’ll first explain what data deduplication is and why it became interesting for researchers. Then I’ll explain how deduplication can be combined with encryption, in particular convergent encryption. This will bring me to the vulnerabilities of CE. Finally I’ll present our solution based on data popularity and perfect hashing.
- Basic idea: store duplicated data only once Explain Mention experiments
- Key and encryption are deterministic
- Researchers noticed that data may need different levels of protection depending on its popularity This assumption works pretty well in all common scenarios, except for a few extreme cases However in our scheme the user can skip the protocol and just encrypt his file Explain when a block becomes popular -> popularity threshold is reached Mention an example
- The problem is shifted to secure popularity detection: if popular do this, if unpopular do that PIR would not be efficient in the case of block-level deduplication Explain that different encryption requires the user to know if data is popular Simple solution -> look for convergent encrypted block -> not secure
- Let’s go into more detail Index does not reveal anything on the block because of collisions Lookup protocols use hash tables, databases use perfect hashing based indices, we need a secure lookup protocol
- We decided to design a new protocol based on perfect hashing Secure because we added the one-wayness property which is foundamental for the security of the protocol
- No confidentiality issue because block is popular
- On the other hand, collisions protect unpopular data Several pre-images corresponding to the same image
- Now let’s have a closer look at the architecture We need a trusted index service in order to handle the popularity transition, that is that phase in which a block that was unpopular becomes popular after reaching a popularity threshold Explain protocol
- Focus on CMPH Mention that we modified CMPH in order to make it secure (one-way)
- Upload of a 10MB file in three different scenarios: file was unpopular, triggered a popularity transition, was popular The take-away from this slide is that all client operations are really lightweight Example: popularity check -> outperforms PIR by far Costly operations are outsourced to the cloud Fix colors
- Outperforms the previous existing solutions