Overview on Privacy Protection in Bitcoin Hot!
the other is to make some modifications with cryptography techniques such as stealth address, ring signature, zero-knowledge proofs, homomorphic encryption. Finally, challenges and perspectives of privacy protection of Bitcoin and Blockchain are discussed.the other is to make some modifications with cryptography techniques such as stealth address, ring signature, zero-knowledge proofs, homomorphic encryption. Finally, challenges and perspectives of privacy protection of Bitcoin and Blockchain are discussed.
Biclique Cryptanalysis of Piccolo Hot!
The lightweight block cipher Piccolo is very efficient for hardware implementation, and its security evaluation has been a hot topic in academia. Based on the idea of biclique attack, combined with the properties of the round function and the key schedule of Piccolo, this paper analyzes the security of Piccolo-80 and Piccolo-128 with unbalanced biclique attack and stars attack. For the unbalanced biclique attack of Piccolo-80, the required data complexity is 236, memory complexity
is 211.12, and computational complexity is 279.03. For the Stars attack of Piccolo-80, the required data complexity is 2, memory complexity is 28.12 and computational complexity is 279.31. For the two attacks of Piccolo-128, the required data complexities are 220 and 2, memory complexities are 211.17 and 28.19, and computational complexities are 2127.05 and 2127.40, respectively. Compared withthe existing attacks, this study takes into consideration of the memory complexity, and makes some optimization in terms of data complexity and computational complexity.
Zero-difference Balanced Functions on Matrix Rings Hot!
Zero-difference balanced (ZDB) functions have many applications in codes and designs, such as the constructions of constant composition codes, constant weight codes, difference systems of sets, and frequency-hopping sequences. Those constructions can meet some optimal bounds if the ZDB functions satisfy some conditions. Most of known ZDB functions are constructed based on cyclotomic cosets over communicative rings. Hence ZDB functions on matrix rings Mn(q) over finite field Fq, are considered in this study. This paper proves that the invertible matrices satisfying certain condition must have its multiplicative order r meeting the condition r|qn-1. Based on this result, new ZDB functions on matrix rings with parameters (qn^2,(qn^2-1)/r+1,r-1), are proposed. Finally,some frameworks of applications of ZDB function are presented, such as the constructions of constant composition codes, constant weight codes, and difference systems of sets. The proposed constructions meet some optimal bounds under some constraints and have many important applications.
Scheme of Block Ciphers Recognition Based on Randomness Test Hot!
Cryptosystem recognition is a process, in the condition of known ciphertext, to classify or recognize the encryption algorithm based on analyzing the feature of the ciphertext. By extracting features and training the model of classification algorithm, machine learning based cryptosystem recognition scheme can construct the cryptosystem classifier to complete its task. This study adopts NIST's randomness test standard to design ciphertext feature extraction method, then proposes a cryptosystem recognition scheme based on random forest. Experimental results show that the scheme can effectively classify plaintexts and ciphertexts or ciphertexts encrypted by block cipher in different mode (ECB mode and CBC mode). For ciphertexts encrypted by different cryptosystems such as AES, Blowfish, Camellia, DES, 3DES, IDEA, the proposed scheme can also give a recognition results which are obviously better than random guess. Compared to existing ciphertext features, some features proposed in this study have good recognition accuracy with less data size. The correlative research can further promote the research on ciphertext feature extraction and cryptosystem recognition.
Preface of Special Issue on Secure Multi-party Computing Technology Hot!
Advances in Practical Secure Two-party Computation and Its Application in Genomic Sequence Comparison Hot!
Secure two-party computation is an important research direction in cryptography. As a special case of secure multi-party computation, secure two-party computation involves only two participants. Compared with the cases of three or more parties, secure two-party computation is more challenging in theory and has a wider range of applications. In recent years, research on practical secure two-party computation has achieved rapid development. Development has made important breakthroughs in the efficiency of generic protocol construction, and has received extensive attention in various applications involving data privacy computation, such as privacy-preserving genomic data analysis. This paper introduces basic concepts and tools of secure two-party computation, and gives a brief overview of some important research results of secure two-party computation in recent years. In addition, the application of secure two-party computation in genomic sequence comparison and its research progress is summarized. For a clear introduction on related work, we start with introducing two major construction techniques (i.e., homomorphic encryption and garbled circuit), and give a clear development direction. In addition, the existing deficiencies in this research area and some possible research directions are pointed out.
Privately Determining Equality of Ranks of Matrix and Its Augmented Ones and Applications Hot!
As a key technique of privacy-preserving and cyberspace security, secure multiparty computation (SMC) is an important research topic in cryptography and is a focus in the international cryptographic community. Secure scientific computation is an important branch of SMC. Matrix is an essential tool of modern science and technology, and it plays significant roles in almost all fields of natural science, engineering, and social science. The rank of a matrix reflects the inherent characteristics of the matrix. Many problems in scientific computation can be induced to the computation of ranks of matrices, and many secure multiparty scientific computations can therefore be induced to the private computation of ranks of matrices. Secure multiparty computation of ranks of matrices is a basic problem of SMC, and is of important theoretical and practical significance. This work studies how to privately determine whether the rank of a matrix is equal to its augmented ones. An efficient protocol is proposed to solve this problem, and the protocol is proved to be secure in the semi-honest model. The proposed protocol can be used as a basic building block to construct many secure multiparty computation protocols, and is further applied to solve other SMC problems, including privately determining the relationship between two lines, determining whether a polynomial divides another one, etc.. The computational and communication complexities are also analyzed, and the feasibility of the scheme are verified by some experiments. Efficiency analysis and experimental data show that the proposed schemes are efficient.
Secure Multiparty Computation of the Maximum and the Minimum in Cloud Environment and Its Statistics Application Hot!
Secure multiparty computation becomes a cryptography research hotspot in recent years. This work mainly studies how to compute the maximum and minimum values securely for some privately input numbers. This is a problem of private-preserving scientific computation. However, so far, very few results are known, and there are no solutions designed for the cloud computing environment. Aiming at these issues, we first adopt 0-1 encoding to encode a private number into an array. This coding technique can hide the confidential data in the array encoded with 0-1. The protocols to compute the maximum and the minimum values are designed by using the multikey NTRU fully homomorphic encryption algorithm in cloud environment. The security of the proposed protocols in this study is analyzed in the semi-honest model, the security proof utilizes the method of simulation paradigm. It is the first time to construct secure computation protocols for the maximum and the minimum values in cloud computing environment, and the solutions can also resist quantum attack. The schemes designed in this study have been adapted to the cloud environment, which can save a large amount of computation cost for users. Finally, the proposed protocols are applied to statistics, and a new problem about the secure multiparty computation of range problem is solved. The solution is simple and secure.
Histogram and Pie Chart of Confidentiality Generation Agreement Hot!
Secure multiparty computation (SMC) is an important aspect of cryptography and a research focus in the international cryptographic community. Though there are universal solutions to secure multiparty computation problems, for the efficiency reason, specific solutions should be developed for specific problems. Although many SMC problems have been investigated, more problems remain to be studied. How to privately generate a histogram or pie chart is a completely new problem which has not been studied. To privately generate a histogram or pie chart, this paper first proposes a new encoding scheme, based on the Paillier additively homomorphic encryption algorithm, and designs a protocol to privately generate a histogram or pie chart. Then a more efficient and more secure protocol is proposed based on elliptic curve additively homomorphic encryption algorithm and threshold encryption algorithms. Finally, the correctness of the proposed protocols are analyzed, and it is proved that these protocols are secure using simulation paradigm in the semi-honest model. The computational complexities and communication complexities of the proposed protocols are analyzed, which shows that these protocols are efficient. The second protocol can resist collision attack of any parties, and the ideas and the protocols in this paper can be used to solve other practical problems.
Smart Contract Execution System over Blockchain Based on Secure Multi-party Computation Hot!
Smart contract is an executable computer protocol in compliance with the terms of interaction among two or more parties, and has legal effect in the real world. It is becoming more and more important as a core technology of Blockchain. However, how to ensure the confidentiality of private information hidden in digital assets and the resistance of smart contract against attacks is an important problem. This study proposes three techniques: a smart contract framework based on secure multi-party computation (SMPC), a fair SMPC algorithm built on linear secret sharing, and a non-blocking message passing interface (MPI). These techniques can be used to guarantee secure group communication after several nodes hit the fault, error, and failure. Moreover, the techniques regulate the workflow, language structure, and syntax specification of SMPC-based smart contract. In addition, the privacy of inputs and the correctness of computing result can be ensured during smart contract execution. Thus, the execution security of smart contract can be enhanced by the proposed techniques in the Blockchain.
Neural Network for Processing Privacy-protected Data Hot!
Neural network is an important data classification tool. A well-trained neural network can efficiently and accurately classify and predict input data. It has extensive applications in information processing and pattern recognition. At present, the study of neural network algorithms and the training of neural networks based on them have become the focus of attention in the industry and academia. Well-trained neural networks have gradually become important intellectual property rights for many data processing companies. Therefore, when ordinary users want to use neural networks for data classification, they often need to host data to professional organizations. How to protect data privacy becomes an important issue. In addition, the current neural network algorithm needs a large amount of computation and cannot run well on devices with limited computing resources. The introduction of cloud computing services provides a solution that can delegate complex neural network computing tasks to the cloud, but there is also the risk of privacy leakage. In order to solve the above problems, a privacy protection neural network based on homomorphic encryption is designed in this paper. The homomorphic encryption algorithm is used to encrypt the data, and the operation process of the neural network is rewritten by means of the properties of homomorphic addition and multiplication. While preserving the data privacy, the designed neural network preserves the computability of the data. Compared with the previous privacy protection neural networks, the proposed one can be applied to complex neural networks with higher security.