scholarly journals Achieving Lightweight Verifiable Privacy Preserving Search Over Encrypted Data

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Selasi Kwame Ocansey ◽  
Charles Fynn Oduro
Keyword(s):  
Service Provider ◽  
Homomorphic Encryption ◽  
Bloom Filter ◽  
Cloud Service ◽  
Privacy Preserving ◽  
Data Sets ◽  
Cloud Service Provider ◽  
Encrypted Data ◽  
Security Proofs

When cloud clients outsource their database to the cloud, they entrust management operations to a cloud service provider who is expected to answer the client’s queries on the cloud where database is located. Efficient techniques can ensure critical requirements for outsourced data’s integrity and authenticity. A lightweight privacy preserving verifiable scheme for outsourcingdatabase securely is proposed, our scheme encrypts data before outsourcing and returned query results are verified with parameters of correctness and completeness. Our scheme is projected on lightweight homomorphic encryption technique and bloom filter which are efficiently authenticated to guarantee the outsourced database’s integrity, authenticity, and confidentiality. An ordering challenge technique is proposed for verifying top-k query results. We conclude by detailing our analysis of security proofs, privacy, verifiability and the performance efficiency of our scheme. Our proposed scheme’s proof and evaluation analysis show its security and efficiency for practical deployment. We also evaluate our scheme’s performances over two UCI data sets.

scholarly journals Two-Party Privacy-Preserving Set Intersection with FHE

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1339
Author(s):  
Yunlu Cai ◽  
Chunming Tang ◽  
Qiuxia Xu
Keyword(s):  
Private Information ◽  
Homomorphic Encryption ◽  
Bloom Filter ◽  
Cloud Service ◽  
Fully Homomorphic Encryption ◽  
Cloud Service Provider ◽  
Set Size ◽  
Set Intersection ◽  
Learning With Errors ◽  
Private Set Intersection

A two-party private set intersection allows two parties, the client and the server, to compute an intersection over their private sets, without revealing any information beyond the intersecting elements. We present a novel private set intersection protocol based on Shuhong Gao’s fully homomorphic encryption scheme and prove the security of the protocol in the semi-honest model. We also present a variant of the protocol which is a completely novel construction for computing the intersection based on Bloom filter and fully homomorphic encryption, and the protocol’s complexity is independent of the set size of the client. The security of the protocols relies on the learning with errors and ring learning with error problems. Furthermore, in the cloud with malicious adversaries, the computation of the private set intersection can be outsourced to the cloud service provider without revealing any private information.

scholarly journals Hail the Closest Driver on Roads: Privacy-Preserving Ride Matching in Online Ride Hailing Services

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Haining Yu ◽  
Hongli Zhang ◽  
Xiangzhan Yu
Keyword(s):  
Service Provider ◽  
Road Network ◽  
Location Privacy ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Smartphone App ◽  
Network Embedding ◽  
Encrypted Data ◽  
Short Notice ◽  
Road Distance

Online ride hailing (ORH) services enable a rider to request a driver to take him wherever he wants through a smartphone app on short notice. To use ORH services, users have to submit their ride information to the ORH service provider to make ride matching, such as pick-up/drop-off location. However, the submission of ride information may lead to the leakages of users’ privacy. In this paper, we focus on the issue of protecting the location information of both riders and drivers during ride matching and propose a privacy-preserving online ride matching scheme, called pRMatch. It enables an ORH service provider to find the closest available driver for an incoming rider over a city-scale road network, while protecting the location privacy of both riders and drivers against the ORH service provider and other unauthorized participants. In pRMatch, we compute the shortest road distance over encrypted data by using road network embedding and partially homomorphic encryption and further efficiently compare encrypted distances by using ciphertext packing and shuffling. The theoretical analysis and experimental results demonstrate that pRMatch is accurate and efficient, yet preserving users’ location privacy.

scholarly journals ALGORITHM FOR TASK CONSOLIDATION IN CLOUD COMPUTING: A COMPARATIVE SURVEY

2018 ◽  
Vol 6 (5) ◽  
pp. 340-345
Author(s):  
Rajat Pugaliya ◽  
Madhu B R
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Resource Utilization ◽  
Service Provider ◽  
Virtual Machines ◽  
Cloud Service ◽  
Cloud Service Provider ◽  
Dynamic Task ◽  
Main Challenge ◽  
High Level

Cloud Computing is an emerging field in the IT industry. Cloud computing provides computing services over the Internet. Cloud Computing demand increasing drastically, which has enforced cloud service provider to ensure proper resource utilization with less cost and less energy consumption. In recent time various consolidation problems found in cloud computing like the task, VM, and server consolidation. These consolidation problems become challenging for resource utilization in cloud computing. We found in the literature review that there is a high level of coupling in resource utilization, cost, and energy consumption. The main challenge for cloud service provider is to maximize the resource utilization, reduce the cost and minimize the energy consumption. The dynamic task consolidation of virtual machines can be a way to solve the problem. This paper presents the comparative study of various task consolidation algorithms.

scholarly journals Energy Efficient Resource Allocation in Cloud Computing

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2071-2074
Keyword(s):  
Virtual Machine ◽  
Service Provider ◽  
Physical System ◽  
Virtual Machines ◽  
Cloud Service ◽  
Cloud Environment ◽  
Cloud Service Provider ◽  
Host System ◽  
Physical Systems ◽  
Single Host

Cloud service provider in cloud environment will provide or provision resource based on demand from the user. The cloud service provider (CSP) will provide resources as and when required or demanded by the user for execution of the job on the cloud environment. The CSP will perform this in a static and dynamic manner. The CSP should also consider various other factors in order to provide the resources to the user, the prime among that will be the Service Level Agreement (SLA), which is normally signed by the user and cloud service provider during the inception phase of service. There are many algorithm which are used in order to allocate resources to the user in cloud environment. The algorithm which is proposed will be used to reduce the amount of energy utilized in performing various job execution in cloud environment. Here the energy utilized for execution of various jobs are taken into account by increasing the number of virtual machines that are used on a single physical host system. There is no thumb rule to calculate the number of virtual machines to be executed on a single host. The same can be derived by calculating the amount of space, speed required along with the time to execute the job on a virtual machine. Based up on this we can derive the number of Virtual machine on a single host system. There can be 10 virtual machines on a single system or even 20 number of virtual machines on single physical system. But if the same is calculated by the equation then the result will be exactly matching with the threshold capacity of the physical system[1]. If more number of physical systems are used to execute fewer virtual machines on each then the amount of energy consumed will be very high. So in order to reduce the energy consumption , the algorithm can be used will not only will help to calculate the number of virtual machines on single physical system , but also will help to reduce the energy as less number of physical systems will be in need[2].

Kernel-Based Machine Learning Models to Predict Mitigation Time During Cloud Security Attacks

2021 ◽  
Vol 17 (4) ◽  
pp. 75-88
Author(s):  
Padmaja Kadiri ◽  
Seshadri Ravala
Keyword(s):  
Machine Learning ◽  
Service Provider ◽  
Predictive Models ◽  
Cloud Service ◽  
Cloud Services ◽  
Security Threats ◽  
Learning Models ◽  
Security Attacks ◽  
Cloud Service Provider ◽  
Machine Learning Models

Security threats are unforeseen attacks to the services provided by the cloud service provider. Depending on the type of attack, the cloud service and its associated features will be unavailable. The mitigation time is an integral part of attack recovery. This research paper explores the different parameters that will aid in predicting the mitigation time after an attack on cloud services. Further, the paper presents machine learning models that can predict the mitigation time. The paper presents the kernel-based machine learning models that can predict the average mitigation time during security attacks. The analysis of the results shows that the kernel-based models show 87% accuracy in predicting the mitigation time. Furthermore, the paper explores the performance of the kernel-based machine learning models based on the regression-based predictive models. The regression model is used as a benchmark model to analyze the performance of the machine learning-based predictive models in the prediction of mitigation time in the wake of an attack.

The Role of Value Facilitation Regarding Cloud Service Provider Profitability in the Cloud Ecosystem

2018 ◽  
pp. 121-142 ◽  
Author(s):  
Alexander Herzfeldt ◽  
Sebastian Floerecke ◽  
Christoph Ertl ◽  
Helmut Krcmar
Keyword(s):  
Service Provider ◽  
Positive Relationship ◽  
Service Providers ◽  
Cloud Service ◽  
Growth Strategies ◽  
Cloud Service Provider ◽  
Cloud Technologies ◽  
The Relationship ◽  
Explorative Interviews

With the increasing maturity of cloud technologies and the growing demand from customers, the cloud computing ecosystem has been expanding continuously with both incumbents and new entrants, whereby it has become more distributed and less transparent. For cloud service providers previously focusing on growth strategies, it is now necessary to shift the attention to providing service efficiently, as well as profitably. Based on 14 explorative interviews with cloud service experts, the relationship between cloud service provider profitability and value facilitation, which stands for the capability to build up resources in advance of future customer engagements, is investigated. The results indicate a positive relationship between cloud service profitability and value facilitation and deliver valuable insights for both researchers and practitioners. In particular, guidelines on how to design profitable cloud service offerings are discussed.

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