Privacy Preservation Based on Separation Sensitive Attributes for Cloud Computing

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.

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Privacy Preservation Based on Separation Sensitive Attributes for Cloud Computing

Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing ◽

10.4018/978-1-7998-5339-8.ch071 ◽

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.

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A Framework for Protecting Users' Privacy in Cloud

Censorship, Surveillance, and Privacy ◽

10.4018/978-1-5225-7113-1.ch021 ◽

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.

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A PSO Algorithm Based Task Scheduling in Cloud Computing

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2019100101 ◽

2019 ◽

Vol 10 (4) ◽

pp. 1-17 ◽

Cited By ~ 2

Author(s):

Mohit Agarwal ◽

Gur Mauj Saran Srivastava

Keyword(s):

Cloud Computing ◽

Task Scheduling ◽

Virtual Machines ◽

Pso Algorithm ◽

Emerging Technology ◽

The Internet ◽

Hard Problem ◽

Swarm Optimization ◽

Np Hard Problem ◽

Simulation Results

Cloud computing is an emerging technology which involves the allocation and de-allocation of the computing resources using the internet. Task scheduling (TS) is one of the fundamental issues in cloud computing and effort has been made to solve this problem. An efficient task scheduling mechanism is always needed for the allocation to the available processing machines in such a manner that no machine is over or under-utilized. Scheduling tasks belongs to the category of NP-hard problem. Through this article, the authors are proposing a particle swarm optimization (PSO) based task scheduling mechanism for the efficient scheduling of tasks among the virtual machines (VMs). The proposed algorithm is compared using the CloudSim simulator with the existing greedy and genetic algorithm-based task scheduling mechanism. The simulation results clearly show that the PSO-based task scheduling mechanism clearly outperforms the others as it results in almost 30% reduction in makespan and increases the resource utilization by 20%.

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A Framework for Protecting Users' Privacy in Cloud

International Journal of Information Security and Privacy ◽

10.4018/ijisp.2016100102 ◽

2016 ◽

Vol 10 (4) ◽

pp. 33-43 ◽

Cited By ~ 1

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.

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A Framework for Protecting Users' Privacy in Cloud

Cyber Law, Privacy, and Security ◽

10.4018/978-1-5225-8897-9.ch023 ◽

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.

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Data Protection in the Clouds

Society and the Internet ◽

10.1093/oso/9780198843498.003.0009 ◽

2019 ◽

pp. 146-164

Author(s):

Christopher Millard

Keyword(s):

Cloud Computing ◽

Data Protection ◽

Personal Information ◽

Personal Data ◽

The Internet ◽

Effective Protection ◽

Legal Perspective ◽

Alternative Approaches ◽

Computing Environments ◽

The Eu

This chapter brings a legal perspective to bear on the topic of data protection on the contemporary Internet in which personal information is increasingly stored and processed in, and accessed from, “the cloud.” The reliance of ever more apps, websites, and services on cloud providers contrasts with earlier days of the Internet in which much more data was stored locally on personal computers. At a time when there is ever more use of cloud computing, this chapter illuminates the complexities over what information in cloud computing environments is protected as personal data, and who is responsible. Will data protection laws, such as those in the EU, protect us, or are there alternative approaches to providing effective protection for personal data in clouds? This chapter airs the question of whether a greater focus should be placed on localizing personal data, as advocated by the Internet pioneer, Tim Berners-Lee.

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Study of the Efficiency of Fog Computing in an Optimized LoRaWAN Cloud Architecture

Sensors ◽

10.3390/s21093159 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3159

Author(s):

Jakub Jalowiczor ◽

Jan Rozhon ◽

Miroslav Voznak

Keyword(s):

Cloud Computing ◽

Fog Computing ◽

The Internet ◽

Long Distance ◽

Daily Lives ◽

Communication Architecture ◽

Computing Paradigm ◽

Data Volume ◽

Distance Communication ◽

Iot Devices

The technologies of the Internet of Things (IoT) have an increasing influence on our daily lives. The expansion of the IoT is associated with the growing number of IoT devices that are connected to the Internet. As the number of connected devices grows, the demand for speed and data volume is also greater. While most IoT network technologies use cloud computing, this solution becomes inefficient for some use-cases. For example, suppose that a company that uses an IoT network with several sensors to collect data within a production hall. The company may require sharing only selected data to the public cloud and responding faster to specific events. In the case of a large amount of data, the off-loading techniques can be utilized to reach higher efficiency. Meeting these requirements is difficult or impossible for solutions adopting cloud computing. The fog computing paradigm addresses these cases by providing data processing closer to end devices. This paper proposes three possible network architectures that adopt fog computing for LoRaWAN because LoRaWAN is already deployed in many locations and offers long-distance communication with low-power consumption. The architecture proposals are further compared in simulations to select the optimal form in terms of total service time. The resulting optimal communication architecture could be deployed to the existing LoRaWAN with minimal cost and effort of the network operator.

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Security and Privacy of Data in Cloud Computing

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b3582.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 6544-6548

Keyword(s):

Cloud Computing ◽

Secret Sharing ◽

Privacy Preservation ◽

Security And Privacy ◽

It Infrastructure ◽

File Size ◽

Privacy And Security ◽

Customer Information ◽

Computing Paradigm ◽

In Transit

The cloud computing paradigm is being used because there is no need to setup additional IT infrastructure such as hardware and software, its low up-front cost. Security and privacy of data is important in day to today life especially for applications that uses cloud computing such as social media. Customer information that is stored at Cloud is crucial that needs to be protected against potential intruders. There is threat to maintain the data in transit and data at cloud due to different possible attacks. Due to this there is growing need of privacy and security of data. In this paper, the privacy and issues, privacy preservation techniques are addressed. In addition to this, in order to protect the data, the secret sharing algorithm is implemented and analyzed. The shamir’s secret sharing (k,n) algorithm is used to split the data into n partial shares which can be distributed in cloud. The user collects at least k partial shares to reconstruct the complete data. It is observed that if the file size is increased, the data recovery time is also increased. The paper concludes with privacy preservation guidelines.

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Fog Computing

Fog Computing ◽

10.4018/978-1-5225-5649-7.ch009 ◽

2018 ◽

pp. 198-207 ◽

Cited By ~ 2

Author(s):

Chintan M. Bhatt ◽

C. K. Bhensdadia

Keyword(s):

Cloud Computing ◽

Internet Of Things ◽

Real Time ◽

Fog Computing ◽

The Internet ◽

Research Issues ◽

Computing Paradigm ◽

Time Interaction ◽

Edge Network ◽

The Internet Of Things

The Internet of Things could be a recent computing paradigm, defined by networks of extremely connected things – sensors, actuators and good objects – communication across networks of homes, buildings, vehicles, and even individuals whereas cloud computing could be ready to keep up with current processing and machine demands. Fog computing provides architectural resolution to deal with some of these issues by providing a layer of intermediate nodes what's referred to as an edge network [26]. These edge nodes provide interoperability, real-time interaction, and if necessary, computational to the Cloud. This paper tries to analyse different fog computing functionalities, tools and technologies and research issues.

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