IJERT-A Novel Approach for Privacy Preservation of Intermediate Datasets in Cloud (original) (raw)
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Enabling for Cost-Effective Privacy Preserving of Intermediate Data Sets in Cloud
In this paper, we propose a upper-bound privacy leakage constraint based approach to identify which intermediate datasets need to be encrypted and which do not, so that privacy preserving cost can be saved while the privacy requirements of data holders can still be satisfied. To identify and encrypt all functionally encrypt able data, sensitive data that can be encrypted without limiting the functionality of the application on the cloud. However, Preserving the privacy of intermediate datasets becomes a challenging problem because adversaries may recover privacy-sensitive information by analyzing multiple intermediate datasets. Encrypting all datasets in cloud is widely adopted in existing approaches to address this challenge. But we argue that encrypting all intermediate datasets are neither efficient nor cost-effective because it is very time consuming and costly for data-intensive applications to encrypt/decrypt datasets frequently while performing any operation on them. In order to preserve privacy, the clients will encrypt their data when they out- source it to the cloud. However, the encrypted form of data greatly impedes the utilization due to its randomness. Such data would be stored on the cloud only in an encrypted form, accessible only to users with the correct keys, thus protecting its confidentiality against unintentional errors and attacks.
Cloud computing offers pay-as-you-go model, where users only pay for their resource consumption. Many large applications utilize cloud computing. These applications generate a lot of essential intermediate results for future purpose. Storing all intermediate results is not a cost efficient approach. At the same time adversary may refer multiple intermediate result to steal the information. Likewise encrypting every part of intermediate results will increase computation cost for the user. The main aim of the system is to provide a cost effective approach for storing and providing privacy for the intermediate results.
Privacy Preserving Based on Encrypting Selective Data Sets in Cloud
International Journal of Engineering Sciences & Research Technology, 2014
Cloud computing provides massive computation power and storage capacity which enable users to deploy computation and data-intensive applications without infrastructure investment. The data are stored in the data sets in the cloud. However, preserving the privacy of intermediate data sets becomes a challenging problem. In existing approaches encrypting all data sets in cloud is widely a greater overhead. So by encrypting all intermediate data sets are neither efficient nor cost-effective. Because it is very time consuming and greater cost consuming. The time consuming and greater cost problem is resolved by encrypting only the selected intermediate data sets. For selecting which intermediate data sets need to be encrypted and which do not, a proposal is made that a novel upper bound privacy leakage constraint-based approach. By using this approach the privacy-preserving cost can be reduced and also time consuming for encryption also get reduced.
Cloud computing provides massive computation power and storage capacity which enable users to deploy computation and data-intensive applications without infrastructure investment. Along the processing of such applications, a large volume of intermediate data sets will be generated, and often stored to save the cost of recomputing them. However, preserving the privacy of intermediate data sets becomes a challenging problem because adversaries may recover privacy-sensitive information by analyzing multiple intermediate data sets. Encrypting ALL data sets in cloud is widely adopted in existing approaches to address this challenge. But we argue that encrypting all intermediate data sets are neither efficient nor cost-effective because it is very time consuming and costly for data-intensive applications to en/decrypt data sets frequently while performing any operation on them. In this paper, we propose a novel upper bound privacy leakage constraint-based approach to identify which intermediate data sets need to be encrypted and which do not, so that privacy-preserving cost can be saved while the privacy requirements of data holders can still be satisfied. Evaluation results demonstrate that the privacy-preserving cost of intermediate data sets can be significantly reduced with our approach over existing ones where all data sets are encrypted.
Towards Cost Effective Approach for Storing Intermediate Datasets in Cloud
Cloud computing offers pay-as-you-go model, where users only pay for their resource consumption. Many large applications utilize cloud computing without infrastructure investment. These applications generate a lot of essential intermediate results for future purpose. Storing all intermediate results is not a cost efficient approach. At the same time adversary may refer multiple intermediate result to steal the information. The preliminary results of this research work shows when Cryptographic techniques such as encryption/decryption are used for providing privacy for intermediate results will increase computation cost but when only sensitive parameters in intermediate results are considered for privacy computational cost is greatly reduced. The main aim of the system is to provide a cost effective approach for storing and providing privacy for the intermediate results
IJERT-Intermediate Data Scheduling in Cloud Environment with Efficient Privacy Preserving
International Journal of Engineering Research and Technology (IJERT), 2013
https://www.ijert.org/intermediate-data-scheduling-in-cloud-environment-with-efficient-privacy-preserving https://www.ijert.org/research/intermediate-data-scheduling-in-cloud-environment-with-efficient-privacy-preserving-IJERTV2IS120505.pdf An alternate approach to supplement the present usage and transport indicate for It organizations reliant upon the Internet, by pleasing continuously flexible and normally virtualized stakes as an organization over the Internet. Data dealing with could be outsourced by the instantaneous Cloud Service Provider (Csp) to distinctive components in the cloud and recommendations substances can in like manner name the assignments to others and so forth. The usage of appropriated processing has unfolded rapidly in various cooperation's. Generally and medium associations use circulated processing organizations for distinctive perspectives, joining since these organizations give fast access to their demands and reduce their structure costs. Cloud suppliers should just address security and security issues as a matter of high and sincere need. Ensuring the security of direct datasets transforms into a testing issue since enemies may recover insurance sensitive information by analyzing various part of the way datasets. Encoding All datasets in cloud is by and large appropriated in existing philosophies to address this test. Distinctive in which direct datasets need to be encoded and which don't, so insurance shielding cost could be saved while the security requirements of data holders can in any case be satisfied. Assurance protecting cost diminishes heuristic count used for security spillage requests and Sensitive Intermediate data set tree/graph (Sit/sig) systems are used.
AN APPROACH FOR COST-EFFECTIVE PRIVACY PRESERVATION IN CLOUDS
In recent the most of the companies are using the cloud to store their huge database. Cloud provides the large space for storage. Cloud is nothing but the pay-as-you-go is used as the economical aspect of privacy-preserving. Privacy for this data is provided by encryption of the data. But there are chances of attack so the violence of the data is possible. For data protection the anonymization and then encryption of data is held on. But whenever the user tries to re-access the data, it should be decrypted. At every time of accessing the encryption and the decryption of the data should be done, which increases the cost of the privacy-preservation also it is the time consuming process as the large number of keys used for encryption and decryption. To reduce this cost of privacy-preservation the privacy leakage constraint is used in which the problem is divided in subproblems and then finding the solution. Then the data is divided into the intermediate datasets. The threshold value is used to privacy-preservation which gives the low cost privacy-preservation. Here the privacy-preserving cost reducing heuristic algorithm is useful for the privacy leakage.
Efficient Technique for Privacy Preserving Publishing of Set Valued Data on Cloud
Cloud computing is an emerging technology to store, handle and access the huge volume of data from anywhere and in anytime. The data in the cloud also contain private information and sensitive information. The concerns of privacy breaches have hindered the development of cloud computing. A data partitioning technique called as extended quasi identifier partitioning (EQI-partitioning)was proposed for privacy preserving in cloud computing. The EQI partitioning technique disassociates the data records which participate in identifying combinations. This technique guaranteed the privacy to cloud data. But this technique protects only the data privacy and it does not considered the information loss and security of cloud data. In this paper, the information loss is considered by using l-diversity and í µí±í µí± í µí±í µí± anonymity in EQI partitioning scheme. In addition to that, a multi level accessibility model is developed to provide the security based on the user's level. The sensitivity value of data stored in cloud computing is computed from the availability, integrity and confidentiality of data. Then identity based proxy re-encryption scheme is used to provide the security for different level of users. Thus the proposed work reduces the information loss and provides the security to data in the cloud. The experimental results are conduced to prove the effectiveness of the proposed work in terms of average relative error, time, anonymization time and information loss.
IJERT-Differential Privacy based Preserving Data on Cloud Environment
International Journal of Engineering Research and Technology (IJERT), 2021
https://www.ijert.org/differential-privacy-based-preserving-data-on-cloud-environment https://www.ijert.org/research/differential-privacy-based-preserving-data-on-cloud-environment-IJERTV10IS050356.pdf Cloud Computing offers several profits, including scalability, accessibility, and many services. But with its wide acceptance everywhere in the world, new risks and penetrability have appeared too. Storing the information on the cloud removes one's worries about space considerations, buying new storage gadgets, or managing their data, rather they're ready to operate their data any time from anywhere on the condition that they need internet access. But the rising security problem holds out against the organizations from connecting with cloud computing completely. Hence, security risks have come out because of the main drawbacks of cloud computing. The paper will include descriptions of approaches to information security and strategies used globally to ensure optimal data protection by reducing threats and risks. It is common that the information is always different from data providers for machine learning. Therefore, how to perform machine learning over cloud data from multiple users becomes a new challenge. Traditional differential privacy techniques and encryption methods are not practical for this environment. On the one hand, the information from different users is encrypted with other public keys or noises, making the computation difficult.
SECURING PUBLISHED DATA IN CLOUD USING DATA INCOGNITO
TJPRC, 2013
Cloud computing is the current hotspot term in the Information Technology (IT) field due to its several profithitting factors like unlimited storage capacity, recovery, backups, low cost, quick improvement in business, and many more. A cloud infrastructure helps small scale industries to grow up and scale up their solutions on a large scale to earn a high income on investments. A Cloud Service Provider (CSP) provides all the services needed by a customer at their end on a pay-as-you go basis. In spite of all these valuable benefits, cloud lacks the security and privacy concerns regarding the published data in the cloud. There has been a lot of research to protect security and privacy of the published data. Generalization and bucketization aresome of the techniques for data security and privacy in cloud. But according to a research survey it has been found that both these methods have some limitations. In both of these methods, a certain amount of data is lost, mainly multi-dimensional data. In this paper we propose a scheme called Data Incognito which helps to improve the security and privacy of published data in cloud.