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Today, we are in the era of big data, and data are becoming more and more important, especially private data. Secure Multi-party Computation (SMPC) technology enables parties to perform computing tasks without revealing original data. However, the underlying implementation of SMPC is too heavy, such as garbled circuit (GC) and oblivious transfer(OT). Every time a piece of data is added, the resources consumed by GC and OT will increase a lot. Therefore, it is unacceptable to process large-scale data in a single SMPC task. In this work, we propose a novel theory called SMPC Task Decomposition (SMPCTD), which can securely decompose a single SMPC task into multiple SMPC sub-tasks and multiple local tasks without leaking the original data. After decomposition, the computing time, memory and communication consumption drop sharply. We then decompose three machine learning (ML) SMPC tasks using our theory and implement them based on a hybrid protocol framework called ABY. Furthermore, we use incremental computation technique to expand the amount of data involved in these three SMPC tasks. The experimental results show that after decomposing these three SMPC tasks, the time, memory and communication consumption are not only greatly reduced, but also stabilized within a certain range.

The payment scheme of Fabric 2.0 alpha is threatened by counterfeit tokens and its users are client apps, not the end consumers. In this paper, a uniform payment system is designed with the following advantages. (1). By limiting the token-minting right to a designated token-minting bank and the design of label, our system prevents the counterfeit token problem. (2). A complete transaction is divided into Bitcoin-level part and Fabric-level part. The Bitcoin-level part constructs inputs and outputs and signs these inputs, outputs and a nonce with a private key. This makes a micro enterprises or an end consumer can enjoy the payment service as the Bitcoin-level operation can be implemented on a mobile phone. The double-spending problem is prevented by the Fabric-level mechanism. (3). The system is easy to expand. When there is a new payment application, only one sub-function is added to the payment contract to implement the payment application, and a call to the sub-function is added. (4). Compared with Bitcoin, the unlockcode of UTXO only needs to be stored once, instead of being stored with each UTXO, thus reducing the sizes of transactions.

We propose a homomorphic search protocol based on quantum homomorphic encryption, in which a client Alice with limited quantum ability can give her encrypted data to a powerful but untrusted quantum server and let the server search for her without decryption. By outsourcing the interactive key-update process to a trusted key center, Alice only needs to prepare and encrypt her original data and to decrypt the ciphered search result in linear time. Besides, we also present a compact and perfectly secure quantum homomorphic evaluation protocol for Cliford circuits, where the decryption key can be calculated by Alice with polynomial overhead with respect to the key length., Comment: 11 pages and 5 figures

Frequent Pattern Mining is a one field of the most significant topics in data mining. In recent years, many algorithms have been proposed for mining frequent itemsets. A new algorithm has been presented for mining frequent itemsets based on N-list data structure called Prepost algorithm. The Prepost algorithm is enhanced by implementing compact PPC-tree with the general tree. Prepost algorithm can only find a frequent itemsets with required (pre-order and post-order) for each node. In this chapter, we improved prepost algorithm based on Hadoop platform (HPrepost), proposed using the Mapreduce programming model. The main goals of proposed method are efficient mining frequent itemsets requiring less running time and memory usage. We have conduct experiments for the proposed scheme to compare with another algorithms. With dense datasets, which have a large average length of transactions, HPrepost is more effective than frequent itemsets algorithms in terms of execution time and memory usage for all min-sup. Generally, our algorithm outperforms algorithms in terms of runtime and memory usage with small thresholds and large datasets., Comment: 11 pages, 10 figures

A compressed Qubits image steganography based on an optimized adaptive neural networks is proposed in this paper, to solve many problems related to adjustment, size and recognition. that are considered inefficient in image information concealing. We used M-PSO algorithm to determine an optimal target vales of the adaptive neural networks to obtain low noise and high accuracy when the results trained. However, our proposed scheme can separately conceal secret message and the content of cover file.

High-level Petri net has been used by more and more researchers for its high-level formalism. In order to facilitate the interoperability between different researchers and tools, we propose a novel logical approach, which is based on description logic and epsiv-connection, to formalizing and analyzing high-level Petri net (HLPN). First, HLPN-ontology (high-level Petri net ontology) is partitioned into algebra-ontology and structure-ontology for reuse and easy analysis. Then, algebra-ontology and structure-ontology are combined with epsiv-connection to express HLPN-Ontology in a compact way. The result of money changing service experiment, which is represented with high-level Petri net, shows that it is more efficient to analyze high-level Petri net with the approach discussed in this paper. The effect of using description logic and epsiv-connection is better than that of using Petri net markup language (PNML), which is an XML-based interchange format but it is not machine-processable.

Recently, Zhang and Lu provided a quantum search algorithm based on partial adiabatic evolution, which beats the time bound of local adiabatic search when the number of marked items in the unsorted database is larger than one. Later, they found that the above two adiabatic search algorithms had the same time complexity when there is only one marked item in the database. In the present paper, following the idea of Roland and Cerf [Roland J and Cerf N J 2002 Phys. Rev. A 65 042308], if within the small symmetric evolution interval defined by Zhang et al., a local adiabatic evolution is performed instead of the original “global one, this “new algorithm exhibits slightly better performance, although they are progressively equivalent with M increasing. In addition, the proof of the optimality for this partial evolution based local adiabatic search when M = 1 is also presented. Two other special cases of the adiabatic algorithm obtained by appropriately tuning the evolution interval of partial adiabatic evolution based quantum search, which are found to have the same phenomenon above, are also discussed.