“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

This document provides updates to IEEE Std 802.16's MIB for the MAC, PHY and asso- ciated management procedures in order to accommodate recent extensions to the standard.

IEEE Standard for Local and Metropolitan Area Networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems Draft Amendment: Management Information Base Extensions

More particularly, calculations are developed and examined to reduce the entire quantity of Wireless access points as well as their locations in almost any given atmosphere while with the throughput needs and the necessity to ensure every place in the area can achieve a minimum of k APs. This paper concentrates on using Wireless for interacting with and localizing the robot. We've carried out thorough studies of Wireless signal propagation qualities both in indoor and outside conditions, which forms the foundation for Wireless AP deployment and communication to be able to augment how human operators communicate with this atmosphere, a mobile automatic platform is developed. Gas and oil refineries could be a harmful atmosphere for various reasons, including heat, toxic gasses, and unpredicted catastrophic failures. When multiple Wireless APs are close together, there's a possible for interference. A graph-coloring heuristic can be used to find out AP funnel allocation. Additionally, Wireless fingerprinting based localization is developed. All of the calculations implemented are examined in real life situations using the robot developed and answers are promising. For example, within the gas and oil industry, during inspection, maintenance, or repair of facilities inside a refinery, people might be uncovered to seriously high temps to have a long time, to toxic gasses including methane and H2S, and also to unpredicted catastrophic failures.

/pdf/internet-of-vehicles-from-intelligent-grid-to-autonomous-pma0tn3ofd.pdf

Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds

The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may not be feasible in realistic application scenarios due to the high scalability of modern IoT networks and growing data privacy concerns. Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need for data sharing. In this article, we provide a comprehensive survey of the emerging applications of FL in IoT networks, beginning from an introduction to the recent advances in FL and IoT to a discussion of their integration. Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing, and IoT privacy and security. We then provide an extensive survey of the use of FL in various key IoT applications such as smart healthcare, smart transportation, Unmanned Aerial Vehicles (UAVs), smart cities, and smart industry. The important lessons learned from this review of the FL-IoT services and applications are also highlighted. We complete this survey by highlighting the current challenges and possible directions for future research in this booming area.

/pdf/federated-learning-for-internet-of-things-a-comprehensive-3ttswrbjdg.pdf

Federated Learning for Internet of Things: A Comprehensive Survey

With the advent of the era of big data, data privacy protection has become a valuable topic in the field of data publication. Unfortunately, traditional methods of privacy protection, k-anonymity, and its extensions are not absolutely secure as an adversary with background knowledge can determine the owner of a record. The emergence of differential privacy provides a reasonable alternative for privacy security, but the existing solutions ignore the correlation between sensitive attributes and other attributes. In this paper, we propose a new differential privacy scheme based on quasi-identifier classification for big data publication (DP-QIC). It is a new data publishing scheme based on the obfuscation of attribute correlation. We innovatively present quasi-identifier classification based on sensitive attributes and the privacy ratio for evaluating the data set vulnerability. DP-QIC achieves data privacy-protecting through four steps: data collection, grouping and shuffling, generalization, merging, and noise adding, which retains the overall statistical characteristics of the data set. Moreover, the exponential mechanism and the Laplace mechanism are integrated to ensure higher flexibility and a stronger level of privacy protection, so DP-QIC can be used for privacy processing of different data groups in future development. Finally, we have compared the performance of our scheme with the other two famous schemes in the industry. Experimental results demonstrate that DP-QIC has obvious advantages in data utility, privacy protection, and processing efficiency.

DP-QIC: A differential privacy scheme based on quasi-identifier classification for big data publication

Cloud storage is one of the most widely-used storage services, because it can provide users with unlimited, scalable, low-cost and convenient resource services. When data is outsourced to cloud for...

An Efficient Privacy-Preserving Attribute-Based Encryption with Hidden Policy for Cloud Storage

In data sharing schemes, the file owners should obtain rewards by sharing files with others as they put energy in these files. Therefore, we proposed an incentive data sharing scheme in this paper which encourages users to share data and also supports customization. Customization allows the owners to decide the threshold of access, the importance of each attributive classification which determines users' priority level of file modification and file ownership obtaining when the original owner leaves according to the priority level value. To support a convincing customized data sharing scheme, we introduce the knowledge of blockchain and construct a suitable access structure based on weighted attributes. The blockchain is used to ensure the fairness in incentive. Based on weighted attributes, an attribute set is disposed to a numerical value and the owner of the attribute set is able to obtain the file when the value is not less than the threshold, which is different from the normal access control policy. We prove the security from integrity, privacy and the availability of access key. The performance of the proposed scheme is evaluated at the end of this paper.

Customized Data Sharing Scheme Based on Blockchain and Weighted Attribute

Nowadays, cloud computing is increasingly popular. As its important application, outsourcing has aroused great concern. Modular exponentiation is an expensive discrete-logarithm operation and it is difficult for users to calculate locally. Therefore, securely outsourcing modular exponentiation to cloud is a good choice for resource-limited users to reduce computation overhead. In this paper, to outsource modular exponentiation calculation, we dope out a fully verifiable secure outsourcing scheme with single server, so as to eliminate the collusion attacks which occur in algorithms based on two untrusted servers. Meanwhile, our algorithm allows outsourcers to detect any misbehavior with probability 1, which means the checkability of our algorithm shows a significant improvement in comparison to other single server based schemes. Furthermore, to protect data privacy, we propose a new division method to hide the primitive outsourced data. Compared with the state-of-the-art schemes, our secure outsourcing algorithm has an outstanding performance in both efficiency and checkability.

New Algorithm for Secure Outsourcing of Modular Exponentiation with Optimal Checkability Based on Single Untrusted Server

In the paper [2010 Chin. Phys. B 19 050306], Yang et al. put forward a novel three-party quantum secret sharing protocol of secure direct communication based on χ-type entangled states, they claimed that the scheme is secure. However, in this paper, we study the security of the protocol and find that it is insecure. Applying intercept and resend attack, the agent Bob can obtain Alice's secret without the help from the other agent Charlie. In the end, we give our effective modification for its improvement.

http://iopscience.iop.org/article/10.1088/1674-1056/21/1/010307/pdf

Anmin Fu

Papers

DP-QIC: A differential privacy scheme based on quasi-identifier classification for big data publication

An Efficient Privacy-Preserving Attribute-Based Encryption with Hidden Policy for Cloud Storage

Customized Data Sharing Scheme Based on Blockchain and Weighted Attribute

New Algorithm for Secure Outsourcing of Modular Exponentiation with Optimal Checkability Based on Single Untrusted Server

Cryptanalysis and improvement of a quantum secret sharing scheme based on χ-type entangled states