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Data processing scheme based on blockchain

In the white paper written on Bitcoin, a chain of blocks was proposed by Satoshi Nakamoto. Since then, blockchain has been rapidly developed. Blockchain is not only limited to the field of cryptocurrency but also has been extensively applied to the Internet of Things, supply chain finance, electronic evidence storage, data sharing, and e-government fields. Both the public chain and the alliance chain have been extensively developed. In the data processing field, blockchain has a particularly good application potential. The Square Kilometre Array (SKA) is a proposal consisting of a joint venture of more than ten countries, resulting in the world’s largest synthetic aperture radio telescope. In the SKA, the processing scale of the data is large, and it consists of several data processing nodes. The data will be processed in the cloud computing mode. Taking the SKA under consideration, this report proposes a data processing scheme based on blockchain for the anti-counterfeiting, anti-tampering and traceability of data. Furthermore, the authenticity and integrity of the data are assured. The primary aspects include data distribution, data operation and data sharing, which correspond to the data reception, data algorithm processing and result sharing of data operation in the SKA. With this process, the integrity, reliability and authenticity of the data are guaranteed. Additionally, smart contracts, homomorphic hashing, secure containers, aggregate signatures and one-way encrypted channels are implemented to ensure the intelligence, security and high performance of the process.


Introduction
Blockchain is a distributed ledger technology [1]. Initially, blockchain was used primarily in the field of cryptocurrency, with Bitcoin being the most common. Litecoin [2], Monroe [3] and Zcash [4] are accepted as well. With the introduction of Ethereum in 2013, the applications of blockchain have expanded, in which the combination of smart contracts and blockchains plays an important role. However, the target of Ethereum is primarily the public blockchain. Due to the low transaction rate of Ethereum and insufficient privacy protection, successful application cases currently include issuing TOKEN and simple games, such as CryptoKitties. In Bitcoin, only seven transactions per second can be handled. Although the performance of Ethereum is better than that of Bitcoin, it can only handle 15-20 transactions per second; thus, it is unable to meet practical demand. Additionally, certain practical applications have higher requirements for privacy protection, which Ethereum cannot currently meet. In 2015, the Hyperledger project was launched, in which the IBM-backed Fabric framework was the most recognized. Fabric is aimed at the alliance blockchain, which essentially meets the needs of practical applications in terms of performance, privacy protection and usability.
With the development of the public and alliance chains, the blockchain application field has rapidly developed. Blockchain has been extensively applied to the Internet of Things [5], supply chain finance, digital data storage certificate, data processing, and e-government [6] fields. In the field of data processing, blockchain guarantees the authenticity, security and reliability of data [7].
Various studies have introduced the use of blockchain for medical data sharing [8], personal data protection [9], and data distribution [10].
Astronomical data have certain characteristics, such as large amounts of data, real-time requirements [11], complicated calculation processes [12], heterogeneous calculation nodes [13], diverse storage models, various data access patterns [14], high expansibility, etc. High performance computing, distributed computing, parallel computing, uniform resource management, container technology and telescope observation control system technology are needed [15]. Current related technologies, such as Apache Hadoop, OpenMP, MPI, etc., all face various problems in processing astronomical data [16]. In the SKA data process, it is necessary to use cloud computing [17]. In distributed data processing, attention should be given to data protection [18]. Therefore, the security of the data distributed storage [19] and the integrity of the data [20] are particularly important.
During data processing, there are extremely high requirements for the synchronization of time [21] and the optimization of algorithm in data merging [22]. Blockchain can play a positive role in ensuring the integrity, security and availability of the data.
In the remainder of this report, Section 2 primarily introduces the data distribution scheme based on blockchain, which reflects the generation and collection of data in the SKA. Section 3 introduces the method of data operation, which reflects the combination of collected data and related algorithms. Section 4 introduces the process of sharing data, which reflects the sharing problem of the result after the original data is processed by the related algorithms. Section 5 summarizes the conclusions of this report.

Preliminaries
In this section, we first define certain notations used in this report. If S is a set, then |S| denotes the number of elements in this set. If b is a real number, thenab  indicates that a = b. If C is a node and c is an element, then Cc  denotes sending c to C. If a and b are two real numbers, then || ab indicates the cascading of a and b.

Bilinear Mapping
1 and 2 are two multiplicative cyclic groups of prime order p, where g1 is a generator of 1 and g2 is a generator of 2. ψ is a computable isomorphism from 2 to 1, with ψ(g2) = g1. A bilinear pairing can be defined as =(n,1,2,T,e,g1,g2) where 1 and T are multiplicative groups of order n. Let e:1×2→T be defined as a map with the following properties: • Bilinear: Then, e is considered bilinear mapping.
Furthermore, the aggregate signature can support incremental aggregation; thus, if σ1 and σ2 can be aggregated to σ12, then σ12 and σ3 can be aggregated to σ123.

Homomorphic Hash
Homomorphism is the mapping of two algebraic structures in abstract algebra that remain structurally constant. There are two groups, 1 and 2, and f is the mapping from 2 to 1. If The homomorphic hash has long been used in peer-to-peer networks [23], which use correction and network codes together against attack events. In a peer-to-peer network, each peer will obtain the original data block directly from the other peers; thus, hash functions such as SHA1 can be used to directly verify the correctness of the received data block by comparing the hash value of the received data block with the original hash value.
Using the homomorphic hash function mentioned in earlier studies [24], i.e., ( ) h  G , a set of hash parameters can be obtained as The parameter description is shown in Table 1. Each of these elements in g can be represented as where ( ) rand  is a pseudo-random function, which can be used as a pseudo-random number generator to initialize the homomorphism hash function parameters in the generating process, generate random numbers in the tag generate process, and determine the random data block in the challenge process, thus creating challenges that can cover the entire data range.
For a block bi, the hash value can be calculated as follows: The hash values of the original block ( ) 12 , , , Given a coding block ej and a coefficient vector ( ) This feature can be used to verify the integrity of a code block. First, the publisher needs to calculate the homomorphic hash values of each data block in advance. The download downloads these homomorphic hash values. Once the verification block is received, its hash value can be calculated using equation (3). Then, equation (4) can be used to verify the correctness of the verification block [25].

Blockchain-based Data Distribution Scheme
Here, we simplify the process of receiving astronomical data in the SKA. The SOURCE represents the original astronomical data, and the Data Receiving Station (DRS) represents the real astronomical data receiving device. The DRS setting is distributed. Different DRSs are responsible for receiving data within their own respective areas. Considering the limitation of the hardware functions, the DRS is only responsible for data reception, temporary storage and data forwarding; it does not participate in data calculation. All data calculation is completed by the Data Processing Node (DPN), which is connected to the blockchain. The concrete architecture is shown in Figure 1.
The method of processing data from the SOURCE to the DRS is relatively simple. It involves processing the data format and setting the storage mode, which is not the focus of this study. Here, the execution process of the DRS to the DPN is introduced.
Furthermore, we use the idea of distributed storage in an IPFS, as shown in Figure 2.
Each block contains a list of trading objects, a link to the previous block, and a hash value for the state tree/database. Additionally, we introduce the method used to import data into a blockchain. Let q be a large prime number. Then, select 12 [26,27]. The specific process is shown in Algorithm 1.
If the equation holds true, then the validation passes; otherwise, the validation fails.
The correctness of this basic framework is given below. Theorem 1 and Theorem 2 provide the correctness of the verification process of a single signature and the correctness of the verification process of an aggregate signature, respectively.

Blockchain-based Data Operation Scheme
The Science Data Processor (SDP) [28] is the SKA Data processing module. The main data is taken from the Central Signal Processor (CSP) module [29], the metadata is taken from the Telescope Manager (TM) module, and the Signal and Data Transport (SaDT) module is responsible for the data transmission. Multiple regional data processing centres will be built. The primary functions of the SDP can be given as follows: • Extract data from the CSP and TM modules • Treat source data as data products that can be used for scientific research • Archive and store data products • Provide access to data products • Control and feedback information to the TM module for a timely challenge observation In the SKA SDP, the two most important computational tasks are FFT [30] and gridding [31].
These two algorithms account for an important part of the total computation, and their efficient implementation provides considerable assistance in the design of the SKA SDP.
As depicted in Figure

Blockchain-based Data Sharing Scheme
The Data Requirement Nodes, which are represented by the public keys   12 , , , r pk pk pk L of the calculation result, are determined in advance through the smart contract. Under intrusive surveillance, the calculated result Re is shared to the nodes represented by these public keys. The shared results, targets and shared time are recorded on the blockchain through the smart contracts.
The concrete architecture is shown in Figure 4.
As shown in Figure 4, the allocation of data is allocated by the data container to each data consumer. In order to ensure the security of data, data allocation adopts the way of single channel.
The data allocation rules are determined by the smart contract of the system.
Before recording on the blockchain, it is necessary to verify the target, and the target verifies the calculated results. If the verification passes, then it is signed. If more than 2/3 of the target's signature is obtained, then the block formed will be recorded on the blockchain. The simple architecture is shown in Figure 5.

Conclusion
This study discusses the data storage, data operation and data sharing methods for large amounts of data processing. Using the blockchain data structure combined with intelligent contracts, homomorphic hashes, secure containers, aggregate signatures and one-way encrypted channels, the authenticity, integrity and reliability of data for the collection, calculation and results sharing of astronomical data is ensured. Combined with the SKA project, this scheme can be applied to astronomical data processing. This method provides innovative ideas for the application of blockchain in the fields of large data volume, rapid data generation, high complexity data processing and high value data processing results.     signature. If more than 2/3 of the target's signature is obtained, then the block formed will be recorded on the blockchain.