Attribute-Based Weighted Keyword Search Scheme Supporting Multi-Search Mechanism in Fog Computing

Author(s):  
Shulan Wang ◽  
Yuan Li ◽  
Haiyan Wang ◽  
Xi Zhang ◽  
Jianyong Chen
2021 ◽  
Vol 14 (1) ◽  
pp. 13
Author(s):  
Volkov Artem ◽  
Kovalenko Vadim ◽  
Ibrahim A. Elgendy ◽  
Ammar Muthanna ◽  
Andrey Koucheryavy

Nowadays, 5G networks are emerged and designed to integrate all the achievements of mobile and fixed communication networks, in which it can provide ultra-high data speeds and enable a broad range of new services with new cloud computing structures such as fog and edge. In spite of this, the complex nature of the system, especially with the varying network conditions, variety of possible mechanisms, hardware, and protocols, makes communication between these technologies challenging. To this end, in this paper, we proposed a new distributed and fog (DD-fog) framework for software development, in which fog and mobile edge computing (MEC) technologies and microservices approach are jointly considered. More specifically, based on the computational and network capabilities, this framework provides a microservices migration between fog structures and elements, in which user query statistics in each of the fog structures are considered. In addition, a new modern solution was proposed for IoT-based application development and deployment, which provides new time constraint services like a tactile internet, autonomous vehicles, etc. Moreover, to maintain quality service delivery services, two different algorithms have been developed to pick load points in the search mechanism for congestion of users and find the fog migration node. Finally, simulation results proved that the proposed framework could reduce the execution time of the microservice function by up to 70% by deploying the rational allocation of resources reasonably.


2021 ◽  
Author(s):  
Israt Jahan Mouri ◽  
Muhammad Ridowan ◽  
Muhammad Abdullah Adnan

Abstract Since more and more data from lightweight platforms like IoT devices are being outsourced to the cloud, the need to ensure privacy while retaining data usability is important. Encrypting documents before uploading to the cloud, ensures privacy but reduces data usability. Searchable encryption, specially public-key searchable encryption (PKSE), allows secure keyword search in the cloud over encrypted documents uploaded from IoT devices. However, most existing PKSE schemes focus on returning all the files that match the queried keyword, which is not practical. To achieve a secure, practical, and efficient keyword search, we design a dynamic ranked PKSE framework over encrypted cloud data named \textit{Secure Public-Key Searchable Encryption} (Se-PKSE). We leverage a partially homomorphically encrypted index tree structure that provides sub-linear ranked search capability and allows dynamic insertion/deletion of documents without the owner storing any document details. An interactive search mechanism is introduced between the user and the cloud to eliminate trapdoors from the search request to ensure search keyword privacy and forward privacy. Finally, we implement a prototype of Se-PKSE and test it in the Amazon EC2 for practicality using the RFC dataset. The comprehensive evaluation demonstrates that Se-PKSE is efficient and secure for practical deployment.


In fog computing outsources the encoded information to many mist hubs on the border of the internet of things (IOT) to reduce delay and network congestion. However, the existing cipher text recovery plan infrequently focus on the fog computing area and most of them still enforce high computational and capacity burden on asset constrained clients.In this writing paper, we tend to better recommended a lightweight small-grained cipher texts search (LFGS) framework in fog calculation by extending cipher text-policy attribute-based encryption (CP-ABE) and searchable encryption (SE) technologies, which can accomplish small-grained fingerprint plus key-word search concurrently. The LFGS can transfer semi calculation and storage burden from clients to picked fog nodes. Furthermore, the fundamental LFGS framework is enhanced to cope with conjunctive keyword search and attribute revise to keep away from returning unrelated search outcomes and unauthorized accesses.


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