{'fraction_non_alphanumeric': 0.024807740014884644, 'fraction_numerical': 0.002976928801786157, 'mean_word_length': 5.420382165605096, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Advances on cloud services and cloud computing

As cloud computing becomes prevalent, many organizations outsource the expensive computing and storage into the cloud servers. This brings many benefits including relief of the burden for storage management, universal data access with independent geographical locations, and avoidance of capital expenditure on hardware, software, and personnel maintenances. Despite the tremendous benefits, the outsource storage inevitably suffers from the security, privacy, trustworthiness, and the access of outsourced data. To address these issues, there has been increased interest to propose and design new algorithms and methodologies for cloud computing and cloud services. This special issue brings together high quality contributions in these research areas. The papers of the special issue have also identified and discussed the challenges in the field that will lead to new solutions and applications for cloud computing and cloud services. The special issue consists of 12 papers organized as follows. In the first paper [1], the authors propose the guidelines of a holistic security model in online collaborative learning through an effective trustworthiness. They consider a parallel processing approach, which can considerably decrease the time of data processing, thus allowing for building relevant trustworthiness models to support learning activities even in real time. The authors used a distributed infrastructure, such as Hadoop and cluster computing, to implement and evaluate the proposed approach for massive processing of log data. The experimental results showed the feasibility of coping with the problem of structuring and processing ill-formatted, heterogeneous, large log files to extract information on trustworthiness indicators and levels from learning groups and ultimately fill a global framework devoted to improve information security in e-learning in real time. In the second paper [2], the authors present a ciphertext-policy attribute-based proxy reencryption scheme for data sharing in public clouds. They proposed a new single-hop unidirectional Ciphertext-Policy Attribute-Based Proxy Re-Encryptio (CP-ABPRE) scheme, which supports attribute-based re-encryption with any monotonic access structure, to tackle the open problem left by the existing CP-ABPRE schemes. The authors showed that the proposed scheme can be proved secure in the random oracle model under the decisional q-parallel bilinear Diffie–Hellman exponentiation assumption. In the third paper [3], the authors propose Ontology as a Service (OaaS), where the ontology tailoring process serves as a service in the cloud. To illustrate OaaS, in this paper, they propose sub-ontology extraction and extension, whereby multiple sub-ontologies are extracted from various source ontologies and then these extracted sub-ontologies are extended to form a complete ontology to be used by the user. The authors use a maximum extraction method to facilitate this. Unified Medical Language System (UMLS) metathesaurus ontology is used as a walk-through case study to illustrate the proposed methods. In the fourth paper [4], the authors present an auditing mechanism for the security of service in grid. They define and analyze some atomic services for grid application based on the service processes of the task. Then, some detecting methods are used for these automatic services. The authors evaluate the proposed auditing mechanism by experiments. The results show that it is feasible to ensure the security of service in grid. In the fifth paper [5], the authors show the performance of Optimized Link State Routing (OLSR) protocol for video streaming application in Vehicular Ad-hoc NETworks (VANETs) cloud computing. The simulations are conducted in urban environment in three scenarios, where roadside access points (APs) are present and not present, with and without buildings. They consider 802.11p standard and send multiple video streaming flows over User Datagram Protocol (UDP). They use