Data Privacy in Remote Data Integrity Checking for Secure Cloud Storage

Transcription

1 Data Privacy in Remote Data Integrity Checking for Secure Cloud Storage Yong Yu Centre for Computer and Information Security University of Wollongong Australia 1

2 Yong Yu, Man Ho Au, Yi Mu, Willy Susilo et al. Enhanced Privacy of a Remote Data Integrity Checking Protocol for Secure Cloud Storage. History of Remote Data Integrity Checking International Journal of Information Security, accepted, Publicly 17 verifiable August, RICPrivacy in RIC protocols Outline Definition of privacy - IND-Privacy Xinyu Fan, Guomin Yang, Yi Mu and Yong Yu, On Indistinguishability Insecurity of existing publicly in Remote verifiable Data RIC protocols Integrity Checking, The Computer Journal, Oxford (accepted, Provably secure RIC protocols with IND-Privacy 19 Oct. 2013), (online version: doi: /comjnl/bxt137) Conclusion

3 Outline Cloud computing and cloud storage Idea of Ateniese et al s PDP Hao et al. s scheme Privacy in RIC protocols Definition of privacy Zero Knowledge Privacy Our RIC protocols with Zero Knowledge Privacy Conclusion

4 Cloud Computing: Advantages Cloud computing enjoys a "pay-per-use model for enabling available, convenient and on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. NIST

5 Cloud Characteristics Cloud Characteristics

6 Cloud Storage vs. Data Integrity Data flow Data owners Data owners Cloud storage service allows owners to outsource their data to cloud servers for storage and maintenance. Low capital costs on hardware and software, low management and maintenance overheads, universal on-demand data access, etc E.g., Amazon S3. 6

7 Cloud Storage vs. Data Integrity Data flow Data owners Data owners Loss of physical control However, data outsourcing also eliminates owners ultimate control over their data. The cloud server is not fully trusted. Try to hide data loss incidents in order to maintain their reputation. Might discard the data that have not been or are rarely accessed for monetary reasons. 7

8 Remote Data Integrity Checking

9 RSA based PDP (Atenises et al, CCS2007) RSA 101 N=pq, p=2p +1, q=2q +1 ed 1mod (N) pk=(e,n) sk=d d Sign : H(m) mod N Verify : e H(m)mod N

10 RSA-based Tag mi TagBlock ti t i (H(W ) g m i d ) mod i N

11 Single Block i (mi,ti) t i (H(W ) g m i d ) mod i N e ti 0 mi e; g H(W ) i m i

12 Challenge-Response m 1 m 2 m 3 m 4 σ 1 σ 2 σ 3 σ 4 m n σ n Client a1 3 a4 T n a a n M a1m 1 a3m3 a4m4 anmn e T 0 M e; g a 1 a3 a (W ) (W ) (W ) 4 a H H H H(W ) n n M

13 Third Party Auditor Privacy against TPA Security against server Shared Data Flow Data Owner Publicly verifiable RIC Cloud Server

14 Privacy Analysis of Ateniese et al. s PDP (T,M) a1 3 a4 T t1 t3 t4 t n a a n M a1m 1 a3m3 a4m4 anmn M1 a11m1 a31m3 a41m4 an 1mn M 2 a12m1 a32m3 a42m4 an2m M 3 a13m 1 a33m3 a43m4 an3m M 4 a14m1 a34m3 a44m4 an4m n n n

15 An improvement due to Hao et al. Zhuo Hao, Sheng Zhong, A Privacy-Preserving Remote Data Integrity Checking Protocol with Data Dynamics and Public Verifiability. IEEE Trans. Knowl. Data Eng. 23(9): (2011)

16 An improvement due to Hao et al.

17 An improvement due to Hao et al.

18 Privacy Analysis of the Scheme Dictionary Attack!!

19 Our improvement System Components

20 Our improvement System Components

21 Our improvement Soundness

22 Our improvement Soundness

23 Our improvement Zero Knowledge Privacy

24 Our improvement Scheme description

25 Our improvement Scheme description TagGen: m m1m 2m3 m, t Z n n

26 Our improvement Scheme description TagGen: m m1m 2m3 m, t Z n n Ivan Damgård, Eiichiro Fujisaki: A Statistically-Hiding Integer Commitment Scheme Based on Groups with Hidden Order. ASIACRYPT 2002:

27 Our improvement Scheme description

28 Our improvement Scheme description Check:

29 Our improvement Soundness Proof

30 Our improvement Soundness Proof

31 Our improvement Soundness Proof If the response can pass the verification,i.e., Using the oracle replay technique and forking lemma, replay H2 to generate a new response R ; then we can get two pairs of collision for H2, we have

32 that is

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36 Our improvement ZK privacy proof

37 Our improvement ZK privacy proof

38 Our improvement ZK privacy proof

39 Conclusions Cloud computing has posed new challenges to data integrity Privacy issues in existing RIC protocols is a big issue. Zero Knowledge Privacy was introduced RSA based publicly verifiable RIC protocols fails to achieve Zero Knowledge privacy An improved scheme with ZK privacy was given

40 Thanks to All