A Framework for Protecting Users' Privacy in Cloud

2019 ◽  
pp. 378-389
Author(s):  
Adesina S. Sodiya ◽  
Adegbuyi B.
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Privacy Preservation ◽  
Personal Data ◽  
Decomposition Algorithm ◽  
Privacy Concerns ◽  
Personally Identifiable Information ◽  
Foreign Key ◽  
Huge Challenge

Data and document privacy concerns are increasingly important in the online world. In Cloud Computing, the story is the same, as the secure processing of personal data represents a huge challenge. The main focus is to preserve and protect personally identifiable information (PII) of individuals, customers, businesses, governments and organisations. The current use of anonymization techniques is not quite efficient because of its failure to use the structure of the datasets under consideration and inability to use a metric that balances the usefulness of information with privacy preservation. In this work, an adaptive lossy decomposition algorithm was developed for preserving privacy in cloud computing. The algorithm uses the foreign key associations to determine the generalizations possible for any attribute in the database. It generates penalties for each obscured attribute when sharing and proposes an optimal decomposition of the relation. Postgraduate database of Federal University of Agriculture, Abeokuta, Nigeria and Adult database provided at the UCIrvine Machine Learning Repository were used for the evaluation. The result shows a system that could be used to improve privacy in cloud computing.

A Framework for Protecting Users' Privacy in Cloud

2016 ◽  
Vol 10 (4) ◽  
pp. 33-43 ◽  
Author(s):  
Adesina S. Sodiya ◽  
Adegbuyi B.
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Privacy Preservation ◽  
Personal Data ◽  
Decomposition Algorithm ◽  
Privacy Concerns ◽  
Personally Identifiable Information ◽  
Foreign Key ◽  
Huge Challenge

Data and document privacy concerns are increasingly important in the online world. In Cloud Computing, the story is the same, as the secure processing of personal data represents a huge challenge The main focus is to to preserve and protect personally identifiable information (PII) of individuals, customers, businesses, governments and organisations. The current use of anonymization techniques is not quite efficient because of its failure to use the structure of the datasets under consideration and inability to use a metric that balances the usefulness of information with privacy preservation. In this work, an adaptive lossy decomposition algorithm was developed for preserving privacy in cloud computing. The algorithm uses the foreign key associations to determine the generalizations possible for any attribute in the database. It generates penalties for each obscured attribute when sharing and proposes an optimal decomposition of the relation. Postgraduate database of Federal University of Agriculture, Abeokuta, Nigeria and Adult database provided at the UCIrvine Machine Learning Repository were used for the evaluation. The result shows a system that could be used to improve privacy in cloud computing.

A Framework for Protecting Users' Privacy in Cloud

2019 ◽  
pp. 479-490
Author(s):  
Adesina S. Sodiya ◽  
Adegbuyi B.
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Privacy Preservation ◽  
Personal Data ◽  
Decomposition Algorithm ◽  
Privacy Concerns ◽  
Personally Identifiable Information ◽  
Foreign Key ◽  
Huge Challenge

Data and document privacy concerns are increasingly important in the online world. In Cloud Computing, the story is the same, as the secure processing of personal data represents a huge challenge. The main focus is to preserve and protect personally identifiable information (PII) of individuals, customers, businesses, governments and organisations. The current use of anonymization techniques is not quite efficient because of its failure to use the structure of the datasets under consideration and inability to use a metric that balances the usefulness of information with privacy preservation. In this work, an adaptive lossy decomposition algorithm was developed for preserving privacy in cloud computing. The algorithm uses the foreign key associations to determine the generalizations possible for any attribute in the database. It generates penalties for each obscured attribute when sharing and proposes an optimal decomposition of the relation. Postgraduate database of Federal University of Agriculture, Abeokuta, Nigeria and Adult database provided at the UCIrvine Machine Learning Repository were used for the evaluation. The result shows a system that could be used to improve privacy in cloud computing.

Privacy Preservation Based on Separation Sensitive Attributes for Cloud Computing

2021 ◽  
pp. 840-856
Author(s):  
Feng Xu ◽  
Mingming Su ◽  
Yating Hou
Keyword(s):  
Cloud Computing ◽  
Privacy Preservation ◽  
Personal Data ◽  
The Internet ◽  
Malicious Nodes ◽  
Privacy Leakage ◽  
Computing Paradigm ◽  
Data Release ◽  
Security Levels ◽  
Simulation Results

The Cloud computing paradigm can improve the efficiency of distributed computing by sharing resources and data over the Internet. However, the security levels of nodes (or severs) are not the same, thus, sensitive tasks and personal data may be scheduled (or shared) to some unsafe nodes, which can lead to privacy leakage. Traditional privacy preservation technologies focus on the protection of data release and process of communication, but lack protection against disposing sensitive tasks to untrusted computing nodes. Therefore, this article put forwards a protocol based on task-transformation, by which tasks will be transformed into another form in the task manager before they can be scheduled to other nodes. The article describes a privacy preservation algorithm based on separation sensitive attributes from values (SSAV) to realize the task-transformation function. This algorithm separates sensitive attributes in the tasks from their values, which make the malicious nodes cannot comprehend the real meaning of the values even they get the transformed tasks. Analysis and simulation results show that the authors' algorithm is more effective.

Privacy Preservation Based on Separation Sensitive Attributes for Cloud Computing

2019 ◽  
Vol 13 (2) ◽  
pp. 104-119 ◽  
Author(s):  
Feng Xu ◽  
Mingming Su ◽  
Yating Hou
Keyword(s):  
Cloud Computing ◽  
Privacy Preservation ◽  
Personal Data ◽  
The Internet ◽  
Malicious Nodes ◽  
Privacy Leakage ◽  
Computing Paradigm ◽  
Data Release ◽  
Security Levels ◽  
Simulation Results

The Cloud computing paradigm can improve the efficiency of distributed computing by sharing resources and data over the Internet. However, the security levels of nodes (or severs) are not the same, thus, sensitive tasks and personal data may be scheduled (or shared) to some unsafe nodes, which can lead to privacy leakage. Traditional privacy preservation technologies focus on the protection of data release and process of communication, but lack protection against disposing sensitive tasks to untrusted computing nodes. Therefore, this article put forwards a protocol based on task-transformation, by which tasks will be transformed into another form in the task manager before they can be scheduled to other nodes. The article describes a privacy preservation algorithm based on separation sensitive attributes from values (SSAV) to realize the task-transformation function. This algorithm separates sensitive attributes in the tasks from their values, which make the malicious nodes cannot comprehend the real meaning of the values even they get the transformed tasks. Analysis and simulation results show that the authors' algorithm is more effective.

Privacy Preservation Based on Separation Sensitive Attributes for Cloud Computing

2021 ◽  
pp. 1449-1465
Author(s):  
Feng Xu ◽  
Mingming Su ◽  
Yating Hou
Keyword(s):  
Cloud Computing ◽  
Privacy Preservation ◽  
Personal Data ◽  
The Internet ◽  
Malicious Nodes ◽  
Privacy Leakage ◽  
Computing Paradigm ◽  
Data Release ◽  
Security Levels ◽  
Simulation Results

The Cloud computing paradigm can improve the efficiency of distributed computing by sharing resources and data over the Internet. However, the security levels of nodes (or severs) are not the same, thus, sensitive tasks and personal data may be scheduled (or shared) to some unsafe nodes, which can lead to privacy leakage. Traditional privacy preservation technologies focus on the protection of data release and process of communication, but lack protection against disposing sensitive tasks to untrusted computing nodes. Therefore, this article put forwards a protocol based on task-transformation, by which tasks will be transformed into another form in the task manager before they can be scheduled to other nodes. The article describes a privacy preservation algorithm based on separation sensitive attributes from values (SSAV) to realize the task-transformation function. This algorithm separates sensitive attributes in the tasks from their values, which make the malicious nodes cannot comprehend the real meaning of the values even they get the transformed tasks. Analysis and simulation results show that the authors' algorithm is more effective.

Privacy Preservation in Cloud Based Cyber Physical Systems

2019 ◽  
Vol 16 (10) ◽  
pp. 4320-4327 ◽  
Author(s):  
Anees Ara
Keyword(s):  
Cloud Computing ◽  
Privacy Preservation ◽  
Cyber Physical Systems ◽  
Security Requirements ◽  
Security And Privacy ◽  
Privacy Concerns ◽  
Physical Systems ◽  
Tightly Coupled ◽  
Time Critical ◽  
Computational Systems

The Cyber physical systems (CPS) are integration of tightly coupled resource constrained physical and computational systems,which have several real time critical care applications. These systems depend on sensors to sense the physical environment. To overcome the limitations of computational power, CPS are supported by cloud computing systems as their backend. The enablement of cloud support to cyber physical cloud computing systemshave seamless advantages of improved efficiency, in same way this leads to major security and privacy concerns. These systems have decentralized and heterogeneous infrastructure and to fulfill all the security requirements, it is certainly a very complex task. In this paper we propose a generalized system model for securing different types of cloud based cyber physical systems, adversary model and holistic literature review of various CPS services, attacks, security challenges and the security schemes that can be applied to overcome a given attacks. At the end of the paper we discuss a case study on securing Medical CPS and compare the applicability of security schemes in this scenario.

scholarly journals Federated Learning-Powered Visual Object Detection for Safety Monitoring

AI Magazine ◽  
2021 ◽  
Vol 42 (2) ◽  
pp. 19-27
Author(s):  
Yang Liu ◽  
Anbu Huang ◽  
Yun Luo ◽  
He Huang ◽  
Youzhi Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Object Detection ◽  
Privacy Preservation ◽  
Safety Monitoring ◽  
Communication Overhead ◽  
Visual Object ◽  
Privacy Concerns ◽  
General Data Protection Regulation ◽  
Actual Usage

Visual object detection is an important artificial intelligence (AI) technique for safety monitoring applications. Current approaches for building visual object detection models require large and well-labeled dataset stored by a centralized entity. This not only poses privacy concerns under the General Data Protection Regulation (GDPR), but also incurs large transmission and storage overhead. Federated learning (FL) is a promising machine learning paradigm to address these challenges. In this paper, we report on FedVision—a machine learning engineering platform to support the development of federated learning powered computer vision applications—to bridge this important gap. The platform has been deployed through collaboration between WeBank and Extreme Vision to help customers develop computer vision-based safety monitoring solutions in smart city applications. Through actual usage, it has demonstrated significant efficiency improvement and cost reduction while fulfilling privacy-preservation requirements (e.g., reducing communication overhead for one company by 50 fold and saving close to 40,000RMB of network cost per annum). To the best of our knowledge, this is the first practical application of FL in computer vision-based tasks.

Supervised Classifier Approach for Intrusion Detection on KDD with Optimal MapReduce Framework Model in Cloud Computing

2019 ◽  
Vol 12 ◽  
Author(s):  
M. Ilayaraja ◽  
S. Hemalatha ◽  
P. Manickam ◽  
K. Sathesh Kumar ◽  
K. Shankar
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Learning Strategies ◽  
Nearest Neighbor ◽  
Detection System ◽  
K Nearest Neighbor ◽  
Mapreduce Model ◽  
The Web

Cloud computing is characterized as the arrangement of assets or administrations accessible through the web to the clients on their request by cloud providers. It communicates everything as administrations over the web in view of the client request, for example operating system, organize equipment, storage, assets, and software. Nowadays, Intrusion Detection System (IDS) plays a powerful system, which deals with the influence of experts to get actions when the system is hacked under some intrusions. Most intrusion detection frameworks are created in light of machine learning strategies. Since the datasets, this utilized as a part of intrusion detection is Knowledge Discovery in Database (KDD). In this paper detect or classify the intruded data utilizing Machine Learning (ML) with the MapReduce model. The primary face considers Hadoop MapReduce model to reduce the extent of database ideal weight decided for reducer model and second stage utilizing Decision Tree (DT) classifier to detect the data. This DT classifier comprises utilizing an appropriate classifier to decide the class labels for the non-homogeneous leaf nodes. The decision tree fragment gives a coarse section profile while the leaf level classifier can give data about the qualities that influence the label inside a portion. From the proposed result accuracy for detection is 96.21% contrasted with existing classifiers, for example, Neural Network (NN), Naive Bayes (NB) and K Nearest Neighbor (KNN).

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