A Public Auditing Framework for Cloud Storage Security

Transcription

1 A Public Auditing Framework for Cloud Storage Security S. Satish Kumar A.Sudheer Kumar D.Venkatesh M.Tech Student (CSE) Assistant Professor Dean of CSE & IT GATES Institute of Tech, Gooty GATES Institute of Tech, Gooty GATES Institute of Tech,Gooty Abstract Commoditization of computing resources has become a reality with cloud computing which is a new computing model. Users are no longer required to invest on procuring computing resources. Instead they use resources provided by cloud service providers in pay as you use fashion. Users are able to use huge storage and processing capabilities of the cloud. However, they feel that they do not have control over data as it is stored in a remote server. Many auditing schemes came into existence for data integrity verification in order to ensure the storage inconsistencies if any are known to end users. Thus they encouraged people to use cloud storage services by providing secure environment. Wang et al. introduced a protocol specification which guarantees security and performance. In this paper we implemented that protocol using Azure cloud. We have developed a prototype that facilitates end user to avail storage and data integrity verification services thus assuring cloud storage security. The empirical results revealed that the protocol is efficient and highly secure. Index Terms Cloud computing, data storage, data integrity, auditing protocol I. INTRODUCTION In the history of IT, cloud computing has brought unprecedented benefits to the computing world. It has made it possible to have a different computing model that does not suffer with scarcity of resources. Cloud computing enables to share computing resources without the need for investment in pay as you use fashion. Cloud service providers such as Microsoft, Oracle, Amazon, Google etc. are able to provide huge clouds which are nothing but computing resources that are provided on demand through Internet [1]. The way IT infrastructure has been used; is changing with the emergence of cloud computing paradigm. One important aspect of cloud computing is that data is stored in a centralized server which is linked to cloud data center. The storage and other services provided by cloud can be utilized by individuals and organizations alike without the need for capital investment. To organizations and individuals cloud provides very useful benefits as they are relieved from storage management, investment, and maintenance [2]. Along with the advantages, it also has challenges in terms of security threats. This is because the users data is stored in a remote server which is considered untrusted. Users are losing control over their data and the storage facilities are under control of cloud service providers. Thus the correctness or integrity of the data is questioned. The cloud data storage might be subjected to internal and external threats. It causes security concern on part of cloud users [3]. Security problems surfaced in cloud computing were known to the world [4], [5], [6]. On the other hand CSPs might have intentions to be unfair towards cloud users and their outsourced data besides hiding security flaws in their storage infrastructure [7], [8]. Out sourcing data to cloud have benefits in the long run provided the security risks are addressed positively. In order to secure cloud data the usage of cryptography to secure data is not feasible as the data is no longer physically stored in the user s machine [9]. At the same time obtaining complete data which has been outsourced for integrity verification is not ideal solution as it is expensive. Cloud users should be able to audit their data without the expensive approach as their systems are resource constrained [10], [7]. The data integrity verification should be done intuitively as that should not give trouble to end Page 450

2 users. The verification process should be inbuilt in cloud so as to enable the owner of data to send integrity verification request to cloud server. Keeping all such things in mind, it is essential to have some sort of public auditing service which enables data owners to verify the data integrity with ease. The public auditing service takes care of periodical verification of data integrity. It helps cloud users to be confident of their outsourced data and cloud service providers can assure storage integrity that makes cloud usage much more popular by improving their services [11]. Many public auditing schemes were introduced for cloud computing [8], [12], [9], [13]. Many such schemes did not consider privacy protection of cloud users except [13]. Encrypting data before outsourcing [4], [9] provides security but adds unnecessary burden on the system. The problems in cryptography might jeopardize the interests of cloud users as it is not suitable solution for secure cloud storage. Therefore it is a wise choice to have public auditing of data dynamics pertaining to each and every user of particular cloud. In [13] introduced homomorpic linear authenticator [8], [12], [7] for enabling public third party auditability. In this paper we implement the protocols provided in [13] for Azure platform in order to make it robust and secure. Our contributions include implementation of privacy preserving protocol for third party auditing; supporting scalable auditing services; improved performance with respect to cloud storage security. The remainder of this paper is organized into the following sections. Section II reviews prior work on privacy preserving public auditing in cloud storage services. Section III provides the proposed security model. Section IV discusses implementation and results while section V concludes the paper. II. PRIOR WORK Considerable research was involved on cloud storage security issues. First of that kind is in [8] proposed by Ateniese et al. [8]which ensures provable data procession. They performed audit on outsourced data using RSA based homomorphic authenticators. One of the schemes proposed by them provides data access to external auditor which may cause security problems. Another security model was proposed by Juels et al. [9]. They used the concept of error correcting codes for proof of irretrievability. The limitation of this solution is that the number of audits is fixed. Moreover this works with only encrypted data. This work was later improved by Bowers et al. [14]. The proof of irretrievability is further studied and improved by Dodi et al. [15]. Afterwards, it is further enhanced with the usage of BLS signatures by Shacham et al. [12]. However, their approaches are not privacy preserving. In order to ensure secure storage and retrieval in cloud computing Shah et al. [16], [11] introduced TPA (Third Pa rty Auditing). Towards it they encrypted data first and then using pre-computed symmetric keyed hashes for auditing purposes. However, this scheme has limitation as it works only with encrypted files. In the recent literature it is found that researcher focuses on data dynamics besides data security which is stored in cloud. First partially dynamic PDP (Provable Data Procession) scheme was introduced by Ateniese et al. [17]. They achieved it using symmetric cryptography. Similar kind of work was done by Wang et al. [18] with some additional feature known as error localization. In their subsequent work [7], they combined techniques such as MHT and HLS for supporting data dynamics in cloud storage. At the same time Erway et al. [19] proposed a scheme for provable data procession with full data dynamics. Linear combination of blocks was used to verify the integrity of cloud storage. Thus [19], [8] and [12] do not providing privacy preserving data integrity. In other prior works, remote data procession protocol was introduced by Sebe et al. [20]. This protocol has no limitations in integrity verification. Across many servers running in distributed environment, data integrity checking was studied by Schwarz and Miller [21]. In the similar fashion Curtmola et al. [22] made experiments on provable data procession in multiple server replicas. In fact they improved the scheme proposed in [8] in order to scale it to multiple server replicas without the need for encoding each replica separately thus providing guaranteed data integrity. Erasure correcting codes were used by Bowers et al. [23] which is an extended model of proof of irretrievability. All the schemes discussed about provide mechanisms for auditing cloud storage. However, they do not meet the true requirements of the privacy preserving auditing. Moreover they do not support batch auditing. Wang et al. [13] proposed privacy preserving public auditing which supports batch auditing too. III. THREAT MODEL AND PROPOSED PROTOCOLS This section provides information about the proposed threat model and also the protocols implemented to ensure privacy preserving public auditing. Threat Model The cloud security threat model considered in this paper is similar to the one used by Wang et al. [13]. It considers three parties in the model. They include cloud data owners, cloud service providers (CSPs) Page 451

3 and third party auditor (TPA). The cloud user or cloud data owner has huge amount of data to be outsourced to cloud. The cloud service provider provides cloud services to cloud data owners while the third party auditor is a trusted entity responsible for auditing of the data of cloud users. The proposed threat model considers the internal and external threats to the cloud data integrity. There might be hardware and software bugs which are hidden by CSP. We also assume that cloud servers do not intentionally disclose the data to third parties. The CSPs are committed to data integrity as per the assumption of this paper. The cloud server is expected to respond to the integrity calls of the TPA in order to verify the data. The communication is done through digital certificates. Figure 1 shows the architecture of the threat model. setup phase and audit phase. In the setup phase, cloud user generates signatures for authentication. Then user takes a file whose number of blocks is known to TPA sends the file and its metadata for verification to server. The local copy may be deleted after outsourcing data. In the audit phase, the following protocol is applied. Fig 2 shows the public auditing protocol implemented in this paper using Azure cloud. Fig. 2 privacy preserving auditing protocol Fig. 1 Cloud storage service architecture (excerpt from [13]) As can be seen in fig. 1 there is data flow between cloud users and cloud servers. Data auditing is done by TPA. Public data auditing is performed by TPA which in privacy preserving fashion. The design goals of the solution provided in [13]include verification of cloud users data in privacy preserving fashion; ensuring storage correctness; ensuring the TPA can t gain access to users data; ensuring that the auditing mechanism is lightweight resulting less computation and communication cost. Proposed Protocols The proposed scheme makes use of four algorithms. They are KeyGen for generation of security keys which is run by cloud user, SigGen for signature generation which is run by cloud user, GenProof is meant for generating proof of correct storage which is run by cloud server, VerifyProof is meant for audit the proof sent by cloud server which is executed by TPA. The proposed scheme assumes that the TPA works in stateless fashion. It works for all cloud users. There are two phases in the scheme namely As can be seen in fig 1, the protocol applied between the cloud user and cloud server is presented. The TPA is capable of verifying the data integrity by challenging the server as per the protocol. Fig. 3 provides the protocol for batch auditing. Fig. 3 Batch auditing protocol Page 452

4 As seen in fig. 3, the batch auditing protocol enables the TPA and cloud server to verify integrity of data of different cloud users concurrently. The batch auditing enables the TPA to work efficiently in real time and serve multiple user sessions simultaneously. However, the TPA works in stateless mode and does not remember the users between the delegations. IV. IMPLEMENTATION AND RESULTS The experiments are made using the prototype application that we have built to test the efficiency of the protocols. The prototype was tested in a PC with 4GB of RAM and Core 2 processor. C# is the programming language used to implement the protocols and facilitate user operations. Microsoft Azure cloud is used to store and retrieve data. The cost of privacy preserving protocol is evaluated with many experiments. Then we focused on batch processing efficiency as well. The quantification of cost enabled us to assess the performance of the protocols implemented in this paper. Fig. 4 shows individual and batch auditing performances. Fig. 4 Performance of individual vs. batch auditing As shown in fig. 4, the horizontal axis represents number of auditing tasks performed while the vertical axis represents the time taken for auditing of each task. It is evident in the graph that when compared with individual auditing, the batch auditing reduces the computation cost by 15%. Fig. 5 - Performance of individual vs. batch auditing with invalid responses As seen in fig. 5, the horizontal axis represents fraction of invalid responses while the vertical axis represents auditing time per task. The results reveal that even when the invalid responses exceed 18% of the batch, the batch auditing performance is still is preferable to individual auditing. V. CONCLUSION In this paper, we have implemented a public auditing protocol in Azure cloud which enables individuals and organizations to store data in cloud without security concerns. The third party auditing protocol we implemented ensures complete storage security as it enables end users to relieve from the security concerns of cloud storage. Our third party auditing protocol does not access to actual data but it is capable of performing audit on data and verifies the integrity of stored data. The protocol helps in auditing data pertaining to concurrent sessions of cloud users. We have built a prototype application that interacts with Azure cloud and demonstrates the efficiency of the proposed system. The empirical results revealed that the proposed protocol provides high security and performance. REFERENCES [1] P. Mell and T. Grance, Draft NIST working definition of cloudcomputing, Referenced on June. 3rd, [2] M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. H. Katz,A. Konwinski, G. Lee, D. A. Patterson, A. Rabkin, I. Stoica,and M. Zaharia, Above the clouds: A Berkeley view of cloud computing, University of California, Berkeley, Tech. Rep. UCBEECS , Feb Page 453

5 [3] Cloud Security Alliance, Top threats to cloud computing, 2010, [4] M. Arrington, Gmail disaster: Reports of mass deletions, 2006, [5] J. Kincaid, MediaMax/TheLinkup closes its doors, July 2008, elinkup-closes-its-doors/. [6] Amazon.com, Amazon s3 availability event: July 20, 2008, [7] Q. Wang, C. Wang, K. Ren, W. Lou, and J. Li, Enabling publicauditability and data dynamics for storage security in cloudcomputing, IEEE Transactions on Parallel and Distributed Systems,vol. 22, no. 5, pp , [8] G. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z. Peterson,and D. Song, Provable data possession at un-trusted stores, in Proc. of CCS 07, 2007, pp [9] A. Juels and J. Burton S. Kaliski, PORs: Proofs of retrievability for large files, in Proc. of CCS 07, October 2007, pp [10] Cloud Security Alliance, Security guidance for criticalareas of focus in cloud computing, 2009, [11] M. A. Shah, R. Swaminathan, and M. Baker, Privacy-preservingaudit and extraction of digital contents, Cryptology eprintarchive, Report 2008/186, [12] H. Shacham and B. Waters, Compact proofs of retrievability, inproc. of Asiacrypt, vol. 5350, Dec 2008, pp [13] Cong Wang, Student Member, IEEE, Sherman S.M. Chow, Qian Wang, Student Member, IEEE,Kui Ren, Senior Member, IEEE, and Wenjing Lou, Senior Member, IEEE, Privacy-Preserving Public Auditing for Secure Cloud Storage, IEEE TRANSACTIONS ON COMPUTERS, [14] K. D. Bowers, A. Juels, and A. Oprea, Proofs of retrievability:theory and implementation, in Proc. of ACM workshop on CloudComputing security (CCSW 09), 2009, pp [15] Y. Dodis, S. P. Vadhan, and D. Wichs, Proofs of retrievability viahardness amplification, in TCC, 2009, pp [16] M. A. Shah, M. Baker, J. C. Mogul, and R. Swaminathan, Auditing to keep online storage services honest, in Proc. of HotOS 07,2007, pp [17] G. Ateniese, R. D. Pietro, L. V. Mancini, and G. Tsudik, Scalable and efficient provable data possession, in Proc. of SecureComm 08,2008, pp [18] C. Wang, Q. Wang, K. Ren, and W. Lou, Towards secure and dependablestorage services in cloud computing, IEEE Transactionson Service Computing, 2011, to appear. [19] C. Erway, A. Kupcu, C. Papamanthou, and R. Tamassia, Dynamic provable data possession, in Proc. of CCS 09, 2009, pp [20] F. Sebe, J. Domingo-Ferrer, A. Mart ınez- Balleste, Y. Deswarte, andj.-j. Quisquater, Efficient remote data possession checking in critical information infrastructures, IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 8, pp , August [21] T. Schwarz and E. L. Miller, Store, forget, and check: Using algebraic signatures to check remotely administered storage, inproc. of ICDCS 06, [22] R. Curtmola, O. Khan, R. Burns, and G. Ateniese, MR-PDP:Multiple-replica provable data possession, in Proc. of ICDCS 08.IEEE Computer Society, 2008, pp [23] K. D. Bowers, A. Juels, and A. Oprea, HAIL: A high-availability and integrity layer for cloud storage, in Proc. of CCS 09, 2009,pp Page 454