Privacy Preserving and Efficient Outsourcing Algorithm to Public Cloud

2018 ◽  
Vol 12 (2) ◽  
pp. 1-25 ◽  
Author(s):  
Malay Kumar ◽  
Manu Vardhan
Keyword(s):  
Data Privacy ◽  
Classification Systems ◽  
Third Party ◽  
Cloud Data ◽  
Computing Model ◽  
Computing Services ◽  
Cloud Server ◽  
Cloud Computing Services ◽  
Cloud Data Centers ◽  
Outsourcing Algorithm

The growth of the cloud computing services and its proliferation in business and academia has triggered enormous opportunities for computation in third-party data management settings. This computing model allows the client to outsource their large computations to cloud data centers, where the cloud server conducts the computation on their behalf. But data privacy and computational integrity are the biggest concern for the client. In this article, the authors attempt to present an algorithm for secure outsourcing of a covariance matrix, which is the basic building block for many automatic classification systems. The algorithm first performs some efficient transformation to protect the privacy and verify the computed result produced by the cloud server. Further, an analytical and experimental analysis shows that the algorithm is simultaneously meeting the design goals of privacy, verifiability and efficiency. Also, found that the proposed algorithm is about 7.8276 times more efficient than the direct implementation.

Privacy Preserving and Efficient Outsourcing Algorithm to Public Cloud

2021 ◽  
pp. 857-878
Author(s):  
Malay Kumar ◽  
Manu Vardhan
Keyword(s):  
Data Privacy ◽  
Classification Systems ◽  
Third Party ◽  
Cloud Data ◽  
Computing Services ◽  
Cloud Server ◽  
Cloud Computing Services ◽  
Cloud Data Centers ◽  
Outsourcing Algorithm ◽  
Direct Implementation

The growth of the cloud computing services and its proliferation in business and academia has triggered enormous opportunities for computation in third-party data management settings. This computing model allows the client to outsource their large computations to cloud data centers, where the cloud server conducts the computation on their behalf. But data privacy and computational integrity are the biggest concern for the client. In this article, the authors attempt to present an algorithm for secure outsourcing of a covariance matrix, which is the basic building block for many automatic classification systems. The algorithm first performs some efficient transformation to protect the privacy and verify the computed result produced by the cloud server. Further, an analytical and experimental analysis shows that the algorithm is simultaneously meeting the design goals of privacy, verifiability and efficiency. Also, found that the proposed algorithm is about 7.8276 times more efficient than the direct implementation.

scholarly journals Security in Cloud Computing Using Hash Algorithm: A Neural Cloud Data Security Model

2018 ◽  
Vol 12 (6) ◽  
pp. 143 ◽  
Author(s):  
Osama Harfoushi ◽  
Ruba Obiedat
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
Data Privacy ◽  
Security Model ◽  
Application Development ◽  
Cloud Data ◽  
Computing Services ◽  
Cloud Computing System ◽  
Cloud Computing Services

Cloud computing is the delivery of computing resources over the Internet. Examples include, among others, servers, storage, big data, databases, networking, software, and analytics. Institutes that provide cloud computing services are called providers. Cloud computing services were primarily developed to help IT professionals through application development, big data storage and recovery, website hosting, on-demand software delivery, and analysis of significant data patterns that could compromise a system’s security. Given the widespread availability of cloud computing, many companies have begun to implement the system because it is cost-efficient, reliable, scalable, and can be accessed from anywhere at any time. The most demanding feature of a cloud computing system is its security platform, which uses cryptographic algorithm levels to enhance protection of unauthorized access, modification, and denial of services. For the most part, cloud security uses algorithms to ensure the preservation of big data stored on remote servers. This study proposes a methodology to reduce concerns about data privacy by using cloud computing cryptography algorithms to improve the security of various platforms and to ensure customer satisfaction.

scholarly journals CP-ABE-Based Secure and Verifiable Data Deletion in Cloud

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Jun Ma ◽  
Minshen Wang ◽  
Jinbo Xiong ◽  
Yongjin Hu
Keyword(s):  
Private Information ◽  
Data Privacy ◽  
Personal Information ◽  
Cloud Data ◽  
Fine Grained ◽  
Computing Services ◽  
Merkle Hash Tree ◽  
Secure Data ◽  
Cloud Computing Services ◽  
Smart Mobile

Cloud data, the ownership of which is separated from their administration, usually contain users’ private information, especially in the fifth-generation mobile communication (5G) environment, because of collecting data from various smart mobile devices inevitably containing personal information. If it is not securely deleted in time or the result of data deletion cannot be verified after their expiration, this will lead to serious issues, such as unauthorized access and data privacy disclosure. Therefore, this affects the security of cloud data and hinders the development of cloud computing services seriously. In this paper, we propose a novel secure data deletion and verification (SDVC) scheme based on CP-ABE to achieve fine-grained secure data deletion and deletion verification for cloud data. Based on the idea of access policy in CP-ABE, we construct an attribute association tree to implement fast revoking attribute and reencrypting key to achieve fine-grained control of secure key deletion. Furthermore, we build a rule transposition algorithm to generate random data blocks and combine the overwriting technology with the Merkle hash tree to implement secure ciphertext deletion and generate a validator, which is then used to verify the result of data deletion. We prove the security of the SDVC scheme under the standard model and verify the correctness and effectiveness of the SDVC scheme through theoretical analysis and ample simulation experiment results.

scholarly journals Producing ‘good enough’ automated transcripts securely: Extending Bokhove and Downey (2018) to address security concerns

2021 ◽  
Vol 14 (1) ◽  
pp. 205979912098776
Author(s):  
Joseph Da Silva
Keyword(s):  
Cyber Security ◽  
English Language ◽  
Negative Impact ◽  
Early Career ◽  
Third Party ◽  
Doctoral Research ◽  
Computing Services ◽  
Cloud Computing Services ◽  
Audio Data

Interviews are an established research method across multiple disciplines. Such interviews are typically transcribed orthographically in order to facilitate analysis. Many novice qualitative researchers’ experiences of manual transcription are that it is tedious and time-consuming, although it is generally accepted within much of the literature that quality of analysis is improved through researchers performing this task themselves. This is despite the potential for the exhausting nature of bulk transcription to conversely have a negative impact upon quality. Other researchers have explored the use of automated methods to ease the task of transcription, more recently using cloud-computing services, but such services present challenges to ensuring confidentiality and privacy of data. In the field of cyber-security, these are particularly concerning; however, any researcher dealing with confidential participant speech should also be uneasy with third-party access to such data. As a result, researchers, particularly early-career researchers and students, may find themselves with no option other than manual transcription. This article presents a secure and effective alternative, building on prior work published in this journal, to present a method that significantly reduced, by more than half, interview transcription time for the researcher yet maintained security of audio data. It presents a comparison between this method and a fully manual method, drawing on data from 10 interviews conducted as part of my doctoral research. The method presented requires an investment in specific equipment which currently only supports the English language.

scholarly journals An enhanced security tree to secure cloud data

2017 ◽  
Vol 7 (1.1) ◽  
pp. 64 ◽  
Author(s):  
S. Renu ◽  
S.H. Krishna Veni
Keyword(s):  
Data Storage ◽  
Binary Tree ◽  
Data Encryption ◽  
Third Party ◽  
Data Outsourcing ◽  
Cloud Data ◽  
Security Issues ◽  
Security Services ◽  
Computing Services ◽  
User Data

The Cloud computing services and security issues are growing exponentially with time. All the CSPs provide utmost security but the issues still exist. Number of technologies and methods are emerged and futile day by day. In order to overcome this situation, we have also proposed a data storage security system using a binary tree approach. Entire services of the binary tree are provided by a Trusted Third Party (TTP) .TTP is a government or reputed organization which facilitates to protect user data from unauthorized access and disclosure. The security services are designed and implemented by the TTP and are executed at the user side. Data classification, Data Encryption and Data Storage are the three vital stages of the security services. An automated file classifier classify unorganized files into four different categories such as Sensitive, Private, Protected and Public. Applied cryptographic techniques are used for data encryption. File splitting and multiple cloud storage techniques are used for data outsourcing which reduces security risks considerably. This technique offers  file protection even when the CSPs compromise. 

scholarly journals Cloud computing service value: A message to the board

2012 ◽  
Vol 43 (4) ◽  
pp. 73-81 ◽  
Author(s):  
R. Von Solms ◽  
M. Viljoen
Keyword(s):  
Information Technology ◽  
Cloud Computing ◽  
New Developments ◽  
Service Value ◽  
Computing Model ◽  
Computing Services ◽  
Cloud Computing Service ◽  
Cloud Computing Services

This paper aims to alert the board to their duty of adding value to the organizations they represent by recognizing opportunities presented by new developments in information technology. Cloud computing is one such development, which is associated with opportunities and benefits. The service value that can be achieved by using this computing model will be influential in the adoption of cloud computing services. Service value is determined by the warranty and utility associated with that service. Thus, if an organization can associate itself with the utility and warranty on offer via cloud computing, it should consider the adoption of these services. Cloud computing is discussed in terms of service value. This promotes an understanding of factors to be considered when making decisions about the adoption of cloud computing.

scholarly journals Enhanced Integrity Checking for Preserve Data Owner and User Level Privacy Using Dual Cryptography Approach

2019 ◽  
pp. 138-146
Author(s):  
Poovizhi. M ◽  
Raja. G
Keyword(s):  
Data Storage ◽  
Data Privacy ◽  
Capital Expenditure ◽  
Data Access ◽  
Third Party ◽  
Configurable Computing ◽  
Local Data ◽  
Cloud Data ◽  
On Demand ◽  
Integrity Checking

Using Cloud Storage, users can tenuously store their data and enjoy the on-demand great quality applications and facilities from a shared pool of configurable computing resources, without the problem of local data storage and maintenance. However, the fact that users no longer have physical possession of the outsourced data makes the data integrity protection in Cloud Computing a formidable task, especially for users with constrained dividing resources. From users’ perspective, including both individuals and IT systems, storing data remotely into the cloud in a flexible on-demand manner brings tempting benefits: 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, etc. To securely introduce an effective Sanitizer and third party auditor (TPA), the following two fundamental requirements have to be met: 1) TPA should be able to capably audit the cloud data storage without demanding the local copy of data, and introduce no additional on-line burden to the cloud user; 2) The third party auditing process should take in no new vulnerabilities towards user data privacy. In this project, utilize and uniquely combine the public auditing protocols with double encryption approach to achieve the privacy-preserving public cloud data auditing system, which meets all integrity checking without any leakage of data. To support efficient handling of multiple auditing tasks, we further explore the technique of online signature to extend our main result into a multi-user setting, where TPA can perform multiple auditing tasks simultaneously. We can implement double encryption algorithm to encrypt the data twice and stored cloud server in Electronic Health Record applications.

scholarly journals A Privacy Preserving Cloud-Based K-NN Search Scheme with Lightweight User Loads

Computers ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 1 ◽  
Author(s):  
Yeong-Cherng Hsu ◽  
Chih-Hsin Hsueh ◽  
Ja-Ling Wu
Keyword(s):  
Data Privacy ◽  
Nearest Neighbor ◽  
Search Algorithm ◽  
Data Access ◽  
Privacy Preserving ◽  
Secret Key ◽  
K Nearest Neighbor ◽  
Sensitive Data ◽  
Cloud Data ◽  
Cloud Server

With the growing popularity of cloud computing, it is convenient for data owners to outsource their data to a cloud server. By utilizing the massive storage and computational resources in cloud, data owners can also provide a platform for users to make query requests. However, due to the privacy concerns, sensitive data should be encrypted before outsourcing. In this work, a novel privacy preserving K-nearest neighbor (K-NN) search scheme over the encrypted outsourced cloud dataset is proposed. The problem is about letting the cloud server find K nearest points with respect to an encrypted query on the encrypted dataset, which was outsourced by data owners, and return the searched results to the querying user. Comparing with other existing methods, our approach leverages the resources of the cloud more by shifting most of the required computational loads, from data owners and query users, to the cloud server. In addition, there is no need for data owners to share their secret key with others. In a nutshell, in the proposed scheme, data points and user queries are encrypted attribute-wise and the entire search algorithm is performed in the encrypted domain; therefore, our approach not only preserves the data privacy and query privacy but also hides the data access pattern from the cloud server. Moreover, by using a tree structure, the proposed scheme could accomplish query requests in sub-liner time, according to our performance analysis. Finally, experimental results demonstrate the practicability and the efficiency of our method.

scholarly journals A Proposed Architecture f or Placement of Cloud Data Centre in Software Defined Network Environment

2021 ◽  
Vol 11 (2) ◽  
pp. 104-116
Author(s):  
Mohit Mathur ◽  
◽  
Mamta Madan ◽  
Mohit Chandra Saxena ◽  
◽  
...  
Keyword(s):  
Data Centers ◽  
Wearable Devices ◽  
Software Defined Network ◽  
Cloud Data ◽  
Data Centre ◽  
Computing Services ◽  
Delay Sensitive ◽  
Smart Watch ◽  
Cloud Computing Services ◽  
Minimum Delay

Emerging technologies like IoT (Internet of Things) and wearable devices like Smart Glass, Smart watch, Smart Bracelet and Smart Plaster produce delay sensitive traffic. Cloud computing services are emerging as supportive technologies by providing resources. Most services like IoT require minimum delay which is still an area of research. This paper is an effort towards the minimization of delay in delivering cloud traffic, by geographically localizing the cloud traffic through establishment of Cloud mini data centers. The anticipated architecture suggests a software defined network supported mini data centers connected together. The paper also suggests the use of segment routing for stitching the transport paths between data centers through Software defined Network Controllers.

Trust, Privacy, Issues in Cloud-Based Healthcare Services

2016 ◽  
pp. 163-188
Author(s):  
Shweta Kaushik ◽  
Charu Gandhi
Keyword(s):  
Healthcare Services ◽  
Third Party ◽  
Cloud System ◽  
Cloud Provider ◽  
Security Issues ◽  
Computing Services ◽  
Data Owner ◽  
Cloud Computing Services ◽  
Patient’S Information ◽  
Privacy Issues

In recent era individuals and organizations are migrating towards the cloud computing services to store and retrieve the data or services. However, they have less confidence on cloud as all the task are handled by the service provider without any involvement of the data owner. Cloud system provides features to the owner, to store their data on some remote locations and allow only authorized users to access the data according to the role, access capability or attribute they possess. Storing the personal health records on cloud server (third party) is a promising model for healthcare services to exchange information with the help of cloud provider. In this chapter, we highlight the various security issues and concerns such as trust, privacy and access control in cloud based healthcare system that needs to be known while storing the patient's information over a cloud system.

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