MCMOD:The Multi-Category Large-Scale Dataset for Maritime Object Detection

The marine environment is becoming increasingly complex due tothe various marine vehicles,and the diversity of maritime objects poses a challengeto marine environmental governance.Maritime object detection technologyplays an important role in this segment.In the field of computer vision,there is no sufficiently comprehensive public dataset for maritime objects inthe contrast to the automotive application domain.The existing maritimedatasets either have no bounding boxes(which are made for object classification)or cover limited varieties of maritime objects.To fulfil the vacancy,this paper proposed the Multi-Category Large-Scale Dataset for MaritimeObject Detection(MCMOD)which is collected by 3 onshore video camerasthat capture data under various environmental conditions such as fog,rain,evening,etc.The whole dataset consists of 16,166 labelled images alongwith 98,590 maritime objects which are classified into 10 classes.Comparedwith the existing maritime datasets,MCMOD contains a relatively balancedquantity of objects of different sizes(in the view).To evaluate MCMOD,this paper applied several state-of-the-art object detection approaches fromcomputer vision research on it and compared their performances.Moreover,a comparison between MCMOD and an existing maritime dataset was conducted.Experimental results indicate that the proposed dataset classifies moretypes of maritime objects and covers more small-scale objects,which canfacilitate the trained detectors to recognize more types of maritime objects anddetect maritime objects over a relatively long distance.The obtained resultsalso showthat the adopted approaches need to be further improved to enhancetheir capabilities in the maritime domain.

Blockchain-Based Privacy-Preserving Public Auditing for Group Shared Data

Cloud storage has been widely used to team work or cooperation devel-opment.Data owners set up groups,generating and uploading their data to cloud storage,while other users in the groups download and make use of it,which is called group data sharing.As all kinds of cloud service,data group sharing also suffers from hardware/software failures and human errors.Provable Data Posses-sion(PDP)schemes are proposed to check the integrity of data stored in cloud without downloading.However,there are still some unmet needs lying in auditing group shared data.Researchers propose four issues necessary for a secure group shared data auditing:public verification,identity privacy,collusion attack resis-tance and traceability.However,none of the published work has succeeded in achieving all of these properties so far.In this paper,we propose a novel block-chain-based ring signature PDP scheme for group shared data,with an instance deployed on a cloud server.We design a linkable ring signature method called Linkable Homomorphic Authenticable Ring Signature(LHARS)to implement public anonymous auditing for group data.We also build smart contracts to resist collusion attack in group auditing.The security analysis and performance evalua-tion prove that our scheme is both secure and efficient.

Blockchain-based Privacy-Preserving Group Data Auditing with Secure User Revocation

Progress in cloud computing makes group data sharing in outsourced storage a reality.People join in group and share data with each other,making team work more convenient.This new application scenario also faces data security threats,even more complex.When a user quit its group,remaining data block signatures must be re-signed to ensure security.Some researchers noticed this problem and proposed a few works to relieve computing overhead on user side.However,considering the privacy and security need of group auditing,there still lacks a comprehensive solution to implement secure group user revocation,supporting identity privacy preserving and collusion attack resistance.Aiming at this target,we construct a concrete scheme based on ring signature and smart contracts.We introduce linkable ring signature to build a kind of novel meta data for integrity proof enabling anonymous verification.And the new meta data supports secure revocation.Meanwhile,smart contracts are using for resisting possible collusion attack and malicious re-signing computation.Under the combined effectiveness of both signature method and blockchain smart contracts,our proposal supports reliable user revocation and signature re-signing,without revealing any user identity in the whole process.Security and performance analysis compared with previous works prove that the proposed scheme is feasible and efficient.

Blockchain-Based Light-Weighted Provable Data Possession for Low Performance Devices

Provable Data Possession(PDP)schemes have long been proposed to solve problem of how to check the integrity of data stored in cloud service without downloading.However,with the emerging of network consisting of low performance devices such as Internet of Things,we find that there are still two obstacles for applying PDP schemes.The first one is the heavy computation overhead in generating tags for data blocks,which is essential for setting up any PDP scheme.The other one is how to resist collusion attacks from third party auditors with any possible entities participating the auditing.In this paper,we propose a novel blockchain-based light-weighted PDP scheme for low performance devices,with an instance deployed on a cloud server.We design a secure outsourced tag generating method for low performance devices,which enables a kind of“hash-sign-switch”two-phase tag computing.With this method,users with low performance devices can employ third party auditors to compute modular exponential operations that accounts for the largest portion of computation overhead in tag generation,without leaking their data content.Chaincodes in blockchain network ensure the correctness of such outsourcing and prevent collusion attacks.The security analysis and performance evaluation prove that our scheme is both secure and efficient.

A survey of cloud network fault diagnostic systems and tool

Recently, cloud computing has become a vital part that supports people’s normal lives and production.However, accompanied by the increasing complexity of the cloud network, failures constantly keep coming up and cause huge economic losses. Thus, to guarantee the cloud network performance and prevent execrable effects caused by failures, cloud network diagnostics has become of great interest for cloud service providers. Due to the characteristics of cloud network(e.g., virtualization and multi-tenancy), transplanting traditional network diagnostic tools to the cloud network face several difficulties. Additionally, many existing tools cannot solve problems in the cloud network. In this paper, we summarize and classify the state-of-the-art technologies of cloud diagnostics which can be used in the production cloud network according to their features. Moreover, we analyze the differences between cloud network diagnostics and traditional network diagnostics based on the characteristics of the cloud network. Considering the operation requirements of the cloud network, we propose the points that should be cared about when designing a cloud network diagnostic tool. Also, we discuss the challenges that cloud network diagnostics will face in future development.