Deep learning can achieve higher accuracy than traditional machine learning algorithms in a variety of machine learning tasks. Recently, privacy-preserving deep learning has drawn tremendous attention from information security community, in which neither training data nor the training model is expected to be exposed. Federated learning is a popular learning mechanism, where multiple parties upload local gradients to a server and the server updates model parameters with the collected gradients. However, there are many security problems neglected in federated learning, for example, the participants may behave incorrectly in gradient collecting or parameter updating, and the server may be malicious as well. In this paper, we present a distributed, secure, and fair deep learning framework named \textit{DeepChain} to solve these problems. DeepChain provides a value-driven incentive mechanism based on Blockchain to force the participants to behave correctly. Meanwhile, DeepChain guarantees data privacy for each participant and provides auditability for the whole training process. We implement a DeepChain prototype and conduct experiments on a real dataset for different settings, and the results show that our DeepChain is promising.

The K-nearest neighbors (KNNs) algorithm is one of Top-10 data mining algorithms and is widely used in various fields of artificial intelligence. This leads to that quantum KNN (QKNN) algorithms have developed and achieved certain speed improvements, denoted as Q- KNN. However, these Q- KNN methods must face two key problems as follows. The first one is that they are mainly focused on neighbor selection without paying attention to the influence of ${K}$ value on the algorithm. The second is that only the neighbor selection process is quantized, and the selection of ${K}$ value is not quantized. To solve these problems, this article designs a novel quantum circuit for KNN classification, so as to simultaneously quantumize the neighbor selection and ${K}$ value selection process. Specifically, the least squares loss and sparse regularization term are first used to construct the objective function of the proposed quantum KNN, so that it can simultaneously obtain the optimal ${K}$ value and ${K}$ nearest neighbors of the testing data. And then, a new quantum circuit is proposed to quantumize the process through quantum phase estimation, controlled rotation, and inverse phase estimation techniques. Finally, experiments are conducted with qiskit and MATLAB to output the quantum and classical results of the algorithm, verifying that the proposed algorithm can output the optimal ${K}$ value and ${K}$ nearest neighbors for each testing data.

Non-negative matrix factorization (NMF) is a powerful tool for data science researchers, and it has been successfully applied to data mining and machine learning community, due to its advantages such as simple form, good interpretability and less storage space. In this paper, we give a detailed survey on existing NMF methods, including a comprehensive analysis of their design principles, characteristics and drawbacks. In addition, we also discuss various variants of NMF methods and analyse properties and applications of these variants. Finally, we evaluate the performance of nine NMF methods through numerical experiments, and the results show that NMF methods perform well in clustering tasks.

Missing value imputation is an actual yet challenging issue confronted by machine learning and data mining. Existing missing value imputation is a procedure that replaces the missing values in a dataset by some plausible values. The plausible values are generally generated from the dataset using a deterministic, or random method. In this paper we propose a new and efficient missing value imputation based on data clustering, called CRI (clustering-based random imputation). In our approach, we fill up the missing values of an instance with those plausible values that are generated from the data similar to this instance using a kernel-based random method. Specifically, we first divide the dataset (exclude instances with missing values) into clusters. And then each of those instances with missing-values is assigned to a cluster most similar to it. Finally, missing values of an instance A are thus patched up with those plausible values that are generated using a kernel-based method to those instances from A's cluster. Our experiments (some of them are with the decision tree induction system C 5.0) have proved the effectiveness of our proposed method in missing value imputation task.