Abstract The rapid development of new technologies such as artificial intelligence and big data analysis requires the simultaneous development of cloud computing technology. The application of IoT‐to‐cloud setting has been fully applied in various industry sectors, such as sensor‐cloud system which is composed of wireless sensor network and cloud computing technology. With the increasing amount and types of collected data, companies need to reduce the dimension of massive data in cloud servers for obtaining data analysis reports rapidly. Due to frequent cloud server data leaks, companies must adequately protect the privacy of some confidential data. To this end, we designed a dimension reduction method for ciphertext data in the sensor‐cloud system based on the CKKS encryption scheme, principal component analysis (PCA) and linear discriminant analysis (LDA) dimension reduction algorithm. As data cannot be directly calculated using traditional PCA and LDA algorithm after encryption, we add some interactive operations and iterative calculations to replace some steps in traditional algorithms. Finally, we select the classification dataset IRIS which is commonly used in machine learning, and screen out the best encryption and calculation parameters, and efficiently realize the dimension reduction method of ciphertext data through a large number of experiments.

Aiming at the shortcomings of the current YOLOv3 model, such as large size, slow response speed, and difficulty in deploying to real devices, this paper reconstructs the target detection model YOLOv3, and proposes a new lightweight target detection network YOLOv3-promote: Firstly, the G-Module combined with the Depth-Wise convolution is used to construct the backbone network of the entire model, and the attention mechanism is introduced and added to perform weighting operations on each channel to get more key features and remove redundant features, thereby strengthening the identification ability of feature network model’s to distinguish target objects among background; Secondly, in order to delete some less important channels to achieve the effect of compressing the model size and improving the calculation speed, the size of the scaling factor gamma in the batch normalization layer is used; Finally, based on NVIDIA’s TensorRT framework model conversion and half-precision acceleration were carried out, and the accelerated model was successfully deployed on the embedded platform Jetson Nano. The performed KITTI experimental results show that the inference speed of our proposed method is about 5 times that of the original model, the parameter volume is reduced to one tenth, the mAP is increased from 86.1% of the original model to 93.1%, and the FPS reaches 25.5fps, realizing the requirements of real-time detection with high precision.

Abstract In order to provide personally oriented solutions in sales strategies for different retailers in the fierce market competition,data mining is being increasingly used in retail industry. We use customer‐oriented data mining technology to extract valuable information from a great amount of historical commercial data, so that we can provide different sales promotions for merchants to extend the business. "Small profits and quick returns" is a popular sales strategy to increase sales volume by many shopping malls. By using statistical modeling, we establish a mathematical model to analysis the relationships among the discount rate,the turnover and the profit margin according to the historical sales data of a shopping mall in recent two years. Base on this,the paper conducts the empirical research on small profits and quick returns and make sales recommendations on promotions. Our analysis indicates that the effect of discount rate on turnover is greater than the effect of profit rate on turnover in the retail industry. It is also necessary to consider the different nature of goods. The final computing results are also influenced by different types of goods. Furthermore, we consider the classification of goods into three types: the sell‐well goods, the common goods, and the unsalable goods and analyze them in the similar framework. The paper shows that there are different characteristics for the different types.

Summary Outsourcing data to cloud servers is a popular service for data owners, however, how to check the integrity and freshness of the outsourced data is very challenge. Recently, Jin et al. proposed a cloud auditing protocol with full integrity and freshness support for cloud data, unfortunately in this article, we show their proposal is not secure. Concretely, the cloud servers can forge the authentication tag and thus has the ability to forge proof of data possession, which obviously invalidates their cloud auditing protocol. We also give a new cloud auditing protocol and analysis its security and performance. The results show our protocol is more efficient and secure.

Public auditing protocol is crucial for the success of cloud computing, as it can ensure the outsourced data in cloud server are not tampered by attackers. Due to its importance, public auditing protocol has received considerable attention in the past years. In 2015, Liu et al. proposed a privacy-preserving public auditing protocol for regenerating-code-based cloud storage (IEEE Transactions on Information Forensics and Security, 10(7):1513-1528, 2015) and claimed it is secure under the considered security model. However, in this article, we will show that their protocol is not as secure as they claimed, i.e., the proxy delegated by the data owner can forge an authenticator for any data block, which obviously invalidates their protocol's security. We hope that by identifying the design flaw, similar weaknesses can be avoided in future protocol design.

Summary Outsourcing datum to the cloud servers is more and more popular for most data owners and enterprises. However, how to ensure the outsourced datum to be kept secure is very important. Especially, how to check the outsourced datum's integrity is a very challenge problem. Until now, there are many cryptographic protocols proposed to solve this problem, such as (dynamic) provable data position protocol, (dynamic) proof of retrievability protocol, etc. Recently, Tian et al proposed a dynamic‐hash‐table‐based public auditing scheme for secure cloud storage, which aims at simultaneously supporting secure dynamic data updating and secure public auditing for cloud storage. However, we find a security flaw in this protocol; concretely, the signature algorithm for the data blocks in their protocol is not secure; the cloud servers can easily modify the outsourced data blocks without detecting. Finally, we give a new protocol by using homomorphic tags based on their protocol and roughly analysis its security.

Summary In the IoT setting, many resource constrained devices outsource their collected data to the Cloud. To ensure the outsourced data has not been lost, these devices need some mechanism to check the integrity of their data. Furthermore, in some settings, ad hoc devices need to act as a group, and in this group, any member may require to verify the integrity of outsourced cloud storage. Aiming at solving this problem, in AISACCS'15, first proposed the concept of group‐based proofs of storage (GPoS). In GPoS, a group manager can authorize data owners as group members, and then these group members can outsource files to the cloud storage server; later, each member can verify the integrity of the outsourced cloud storage. They also give a concrete construction of GPoS. Unfortunately, in this paper, we find their scheme is not secure. Recently, also proposed a dynamic group‐based integrity auditing protocol for outsrouced cloud storage; we also show their scheme is not secure either. Finally, we give an improved scheme and roughly analysis to its security and performance.

Recently how to audit cloud storage correctly and securely is a very hot research topic for cloud storage. At Infocom 2015, Chen et al. proposed a novel secure cloud storage audit protocol based on distributed string equality checking, which is a very interesting result. In this paper, we give some improvements to their protocol to strengthen its security. In particular, we show that if the data owners reuse pseudorandom function on the same order number of file block when uploading file block’s tag to the cloud, the protocol maybe not secure any more. We also show that there can exist many (mi, ci) pairs which will result in the same inner product sum ∑i=1nmi⋅ci, while mi denotes the file block, ci denotes the challenge value, and n denotes the number of blocks. We emphasis here that the flaw maybe does not lie in the traditional security model of cloud storage auditing protocol, but we think it is actually a serious drawback for current secure cloud storage audit protocols. We emphasis these two flaws can also exist in some famous cloud auditing protocols such as PDP and POR. Finally, we give an improvement to their protocol, and thus also give a way to strengthen the famous PDP and POR protocols.