tFromBytes(paramArrayOfByte) Įlement localElement2 = localPairing.getZr().newElement().setToRandom() Įlement localElement3 = localPairing.getGT().newElement() ParamArrayOfByte, AccessTree paramAccessTree)Įlement localElement1 = localPairing.getGT().newElement() Public static ABECiphertext encrypt(PublicKey paramPublicKey, byte Public static byte encrypt(byte paramArrayOfByte)ĪrrayOfByte = cipher.doFinal(paramArrayOfByte) LinkedList linkedlist = new LinkedList()
Timing = ABECipher.timing + ABECipher.timing + ABECipher.timing PublicKey publickey = new PublicKey(i, s) īyte abyte1 = AESCipher.encrypt(abyte0) ĪBECiphertext abeciphertext = ABECipher.encrypt(publickey, AESCipher.key, accesstree) Public List encrypt(byte abyte0, int i, String s, String s1)ĪccessTree accesstree = new AccessTree(s1) List = cipher.encrypt(data, secLevel,type, policy) Public static List encrypt(String policy, int secLevel, String type,ĭETABECipher cipher = new DETABECipher() On the other hand, without duplicating the ledger across the nodes, it has raised a question on how IOTA can warrant for an immutable transaction without replicating its transaction across the network. This data structure is reasonably for IOTA as a use case for the IoT, which has limited resources to duplicates transaction in all nodes. IOTA is employing DAG and storing its transaction on each node, and its Block is a single ledger of all nodes. This ambiguous definition is critical to this Systematic Literature finding that a block of transactions size is profoundly impacting its performance and also its use case. This contradictory definition explained the principle of DAG data structure is of differing view of nodes, and the numbers of transaction storing in each node are of different size and numbers. Hedera Hashgraph defined transactions as a signed transaction created by any member and subsequently distributed a copy of the transaction to all its member, which is the nodes (Baird,2019). However, Hedera has also employed the DAG structure, and its whitepaper explained that blockchain and Hashgraph ledgers are commonly using the same principle where created transactions are stored into a container or Block and then synchronised through the distributed network (2019). As described earlier by Saad et al., DAG does not duplicate its transactions over the entire network. has explicitly explained the differences between Blockchain and DAG and also classifying that Blockchain is used by Ethereuem as the data structure and also disclaimed that this does not apply to its “coins”. In other words, it is not the smart contract nor its transaction that Ethereum is employing DAG to provide non-blockchain consensus. In this connection, I have the following questions.Īntonopolous and Wood explained that the cryptocurrency maintains its dataset in DAG is to enable high data access frequency without slowing the performance by retrieving its entire large dataset (2019). After reading the relevant literature, I believe that my survey respondents are nested within regions which points to the hierarchal data structure. I want to simultaneously analyze the individual and contextual factors to identify the important determinants of radicalization. Secondary data on these variables for just one point of time is available from the national statistical office, i.e. However, radicalization usually takes place in specific settings and therefore contextual factors, such as regional differences in economic development, regional gross domestic products, health facilities, government capacity, etc., are also of key importance. The survey questions measure individuals' preception and I, therefore, term these as individual-level factors. The respondents of the survey belong to three different regions of the province (North, Center, and South). The survey has 24 explanatory variables each of which is measured through several questions/items on 5-point Likert scales. The dependent variable is radicalization which is measured through 15 survey questions/items (5-point Likert scale). I am conducting a research project that aims to assess the determinants of radicalization/extremism.
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