Availability Of Usage Data Research Articles

Multiverse Recurrent Expansion With Multiple Repeats: A Representation Learning Algorithm for Electricity Theft Detection in Smart Grids

Electricity theft, known as “Non-Technical Loss” (NTL) is certainly one of the priorities of power distribution utilities. Indeed, NTL could lead to serious damage ranging from massive financial losses to loss of reputation resulting from poor power quality. With advances in metering infrastructure technologies, the availability of user data has fueled the emergence of data-driven methods in NTL detection. Among these methods, deep learning (DL) is an indisputable alternative to conventional human-centric approaches. Typically, modeling based on NTL data is subject to three main challenges, including (i) missing information; (ii) class imbalance; and (iii) data complexity. In this context, this paper contributes to solving these three main problems while paying more attention to data complexity related to cardinality. Accordingly, a multiverse recurrent expansion with multiple repeats (MV-REMR) algorithm is proposed in this paper. MV-REMR is able to provide deeper representations than ordinary DL networks and take advantage of different trained deep network responses to build an efficient model. For MV-REMR efficiency analysis, a realistic NTL dataset is considered. As a result, MV-REMR has shown that it can achieve what is considered excellent feature mapping proven by both scatter visualization and variations in widely used classification metrics. Moreover, MV-REMR shows its ability to marginalize the distance of data classes with superior performance. In addition, thanks to the new mapping scheme, MV-REMR shows its ability to correct outliers resulting from errors in missing values filling techniques. Finally, a comparison with some recent successful works also confirms the superiority of the MV-REMR model.

DPTP-LICD: A differential privacy trajectory protection method based on latent interest community detection

With the rapid development of high-speed mobile network technology and high-precision positioning technology, the trajectory information of mobile users has received extensive attention from academia and industry in the field of Location-based Social Networks. Researchers can mine users’ trajectories in Location-based Social Networks to obtain sensitive information, such as friendship groups, activity patterns, and consumption habits. Therefore, mobile users’ privacy and security issues have received growing attention in Location-based Social networks. It is crucial to strike a balance between privacy protection and data availability. This paper proposes a differential privacy trajectory protection method based on latent interest community detection(DPTP-LICD), ensuring strict privacy protection standards and user data availability. Firstly, based on the historical trajectory information of users, spatiotemporal constraint information is extracted to construct a potential community strength model for mobile users. Secondly, the latent interest community obtained from the analysis is used to identify preferred hot spots on the user’s trajectory, and their priorities are assigned based on a popularity model. A reasonable privacy budget is allocated to prevent excessive noise from being added and rendering the protected trajectory data unusable. Finally, to prevent privacy leakage, we add Laplace and exponential noise in generating preferred hot spots and recommending user interest points. Security and effectiveness analysis shows that our mechanism provides effective points of interest recommendations and protects users’ privacy from disclosure.

Themis: An accountable blockchain-based P2P cloud storage scheme

Cloud storage is an effective way for data owners to outsource their data by remotely storing them in the cloud and enjoy on-demand high quality services. In traditional cloud storage systems, cloud data integrity verification relies on centralized entities and data is stored in a small number of storage servicers. However, these centralized entities and storage servicers may be untrustworthy, and malicious servicers may even refuse to perform a user’s query or update request. Though a few blockchain-based themes have been proposed to address some of these problems, they do not achieve decentralization, accountability, flexibility and practicability simultaneously. In this paper, we present Themis, an accountable P2P cloud storage scheme with smart contracts on Ethereum. Our scheme has the following advantages: First, cloud data integrity verification is decentralized and implemented by miners on blockchain without any trusted third party. Second, by carefully setting up the reward and punishment mechanism within a smart storage contract, all rational nodes will participate in the storage service following an accountable rule. Third, based on reliable information published on the blockchain, users are free to choose appropriate storage servicers who want to share idle storage, making storage service decentralized and flexible. Fourth, compared with the existing related systems, by adopting a payment at maturity method, the malicious behavior of breaking the contract after the servicer obtains some revenue is prevented, and the availability of user data within the specified period is enhanced. Fourthermore, we implement a prototype of Themis on Rinkeby, an Ethereum test network. Extensive experimental results demonstrate that our scheme is able to support a PB-level data storage in a single P2P storage service at a low cost and is feasible for practical deployment. And the entire decentralized arbitration process takes only 40 to 110 seconds.

A Method for RAID Availability Analysis Based on Bernoulli Trials

With the processing speeds of processors increasing rapidly, the inefficiency of disk I/O performance has been becoming the bottleneck of computer systems and network transmission. So, Redundant Arrays of Independent (or Inexpensive) Disks (RAID) with the purpose of promoting disks parallel accessing , disks space utilization and user data availability has been adopted by DAS and SAN, and has been applied widely. However, the more quantity and capacity that disks have, the more mistakes that would take place, so RAID availability analysis has become an important and urgent issue. The paper proposed a set of availability computational modeling for RAID storage system via Bernoulli trials and binomial distribution. On the basis of it, it can be analyzed that combination orders and methods have an effect on RAID availability in the state of mixed combination; it also can be analyzed that disk quantity is associated with RAID availability. Both of them provide powerful decision support and theoretical foundation for user selecting RAID construction solution, and help users save cost of storage.