An Effective Integrity Check Scheme for Secure Erasure Code-Based Storage Systems

Abstract

In the application of cloud storage, a user no longer possesses his files in his local depository. Thus, he is concerned about the security of the stored files. Data confidentiality and data robustness are the main security issues. For data confidentiality, the user can first encrypt files and then store the encrypted files in a cloud storage. For data robustness, there are two concerns: service failure, and service corruption. We are concerned about data robustness in cloud storage services. Lin and Tzeng proposed a secure erasure code-based storage system with multiple key servers recently. Their system supports a repair mechanism, where a new storage server can compute a new ciphertext from the ciphertexts obtained from the remaining storage servers. Their system considers data confidentiality in the cloud, and data robustness against storage server failure. In this paper, we propose an integrity check scheme for their system to enhance data robustness against storage server corruption, which returns tampered ciphertexts. With our integrity check scheme, their storage system can deal with not only the problem of storage server failure, but also the problem of storage server corruption. The challenging part of our work is to have homomorphic integrity tags. New integrity tags can be computed from old integrity tags by storage servers without involvement of the user's secret key or backup servers. We prove the security of our integrity check scheme formally, and establish the parameters for achieving an overwhelming probability of a successful data retrieval. Index Terms-Dataconfidentiality, data robustness, homomorphism, integrity check, secure decentralized erasure code. NOTATION The security parameter The public parameter A -bit prime The operation of concatenating string and string into another string The operation of choosing element from set randomly and uniformly The bit size of element The number of storage servers The number of data blocks The number of key servers The threshold number of secret sharingThe number of storage servers that each ciphertext-tag tuple is dispatched to in the storing phase The number of storage servers that each key server queries in the retrieval phase The number of storage servers that a key server queries in the integrity check phase The number of storage servers that are failed or corrupted among the queried storage servers The proportion of storage servers that are failed or corrupted in the storage system The proportion of storage servers that are failed or corrupted among the queried storage servers: The predefined threshold for such that data are retrievable with overwhelming probability if Two small positive values such that if The public key of userThe secret key of userThe master verification key of userThe th storage serverThe th key serverThe identifier of data The share of secret key for key serverThe verification key of user for dataThe th block of dataThe ciphertext-tag tuple of blockThe encoded tuple stored in storage server 0018-9529

A secure regenerating code‐based cloud storage with efficient integrity verification

“…() But replication introduces high storage overhead since multiple copies of the data are stored at different storage servers. Data IV schemes for erasure code‐based cloud storage system are presented in Bowers et al and Shen et al() However, erasure code‐based storage systems suffer from high repair bandwidth because the original file distributed among different servers needs to be reconstructed in order to recover the fraction of the file stored in the corrupted server.…”

“…Several auditing schemes have been proposed in the recent years for different types of cloud storage settings. () In these schemes, homomorphic authentication tags are outsourced along with the data blocks to check data integrity with reduced communication overhead. Because of the homomorphic property of the authentication tags, the tag computed on the aggregated data blocks will be equal to the aggregated tags.…”

Securing Coding-Based Cloud Storage Against Pollution Attacks

“…Cryptographic or algebraic based approached to design on-the-fly verification techniques of the received coded fragments is another line of research proposed and discussed by several papers, e.g., [6], [7], [8], [9], [10], [11], [12], [13], [14]. High computational costs for verification and remarkable communication overhead due to pre-distribution of verification information represent limitations of these approaches.…”

“…Then, the algorithm enters a loop (lines [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] in which -at each iteration -uses the Decode method (Alg. 1) to check whether the decoding of the fragments contributed only by nodes in W gives rise to a polluted sector (line 10).…”

“…• the SNs in P are actual polluters (for loop in lines [3][4][5][6][7][8][9][10][11][12][13][14]. This check is performed by decoding a working set Y, composed of one polluter from P and all but one honest node from H to verify that pollution actually occurs.…”