Secured Data Sharing in Groups Using Attribute-Based Broadcast Encryption in Hybrid Cloud

Poornima, E.; Kasiviswanath, N.; Bindu, C. Shoba

doi:10.1007/978-981-13-2285-3_83

Cited by 2 publications

(3 citation statements)

References 10 publications

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“…In this section, proposed method for describing the mathematical model of encryption in (5). Data transfer or a message M, the public key PK, and a collection of attributes I are the inputs for this method.…”

Section: Encryption Time (Et)mentioning

confidence: 99%

“…A technique based on attribute-based broadcast encryption is provided in [4] to secure group information exchange using attribute-based broadcast encryption. Cloud computing system included the internet of things as a foundational event for big data [5]. In order to increase security concerns, it also made an effort to develop an architectural handing-off on the organization's security.…”

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confidence: 99%

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Flexible and secure continues data transmission among multiple users in cloud environment

Elumalai

Dinakaran

2023

IJEECS

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Cloud computing is an internet based computing where the sharable information, software and resources are provided based on demand devices. Where, the rapid development and pervasive growth of unavoidable sending of message advances, there are expanding requests of adaptable cryptographic natives to protected group data transactions and computing platforms in cloud. Group key agreement (GKA) protocol enables a group to share a standard encryption key across an open network so that only members of the group may decode the ciphertexts encoded using the secret encryption key that has been released. However, a sender cannot deny any specific member from decryptions the ciphertexts in cloud. However, before sending a message to a group, a user must join the group and follow the GKA protocol to provide the intended members access to a secret key. To find a better solution for the above-mentioned issues, flexible and secure continues data transmission (FSCDT) algorithm is proposed to offer dynamic and secure data transfer broadcasting without full trust of key authority in unreliable cloud environment. It provides compete security proof, outlines the requirements of the aggregatability of the secret attribute based FSCDT building block. Based on experimental evaluations, FSCDT algorithm minimizes encryption time, decryption time and communication cost.

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Section: Encryption Time (Et)mentioning

confidence: 99%

mentioning

confidence: 99%

Flexible and secure continues data transmission among multiple users in cloud environment

Elumalai

Dinakaran

2023

IJEECS

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“…These attacks are difficult to be detected. To avoid the attacks on BSR protocol, proposed two schemes namely Reverse Routing Scheme (RRS) and Authentication of Nodes Scheme (ANS).Authors [13] presented the key issues of data security in the cloud computing technology, and are described in four areas: storage security, network security, data security and virtualization.Authors [14] has discussed the importance of data sharing and the necessity to ensure security and privacy.…”

Section: Literature Surveymentioning

confidence: 99%

Viable Detection of URL Phishing using Machine Learning Approach

Santoshi Kumari,

Keerthi Priya,

Bhavya

et al. 2023

E3S Web Conf.

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The objective of paper is to detect phishing URLs using machine learning algorithms. Phishing is a fraudulent activity that involves tricking users into giving away sensitive information, such as passwords and credit card numbers, by impersonating legitimate websites. The main objective of this work is to build a model that can accurately detect viable phishing URLs and classify them as either legitimate or fraudulent. This will help to prevent users from falling victim to phishing attacks and protect their personal information. The model will be trained on a large dataset of annotated URLs and will be optimised for high accuracy and low false positive rates. The paper consists of two datasets in which one of the dataset consists of phishing URLs and other datasets consist of features of URLs. The performance of the phishing detection model will be evaluated using various metrics, such as precision, recall, and F1 score. We will also conduct an in-depth analysis of the results and discuss the effectiveness of the approach. This work aims to build a robust model for phishing URL detection using machine learning algorithms. Future enhancements to this work could include incorporating more advanced feature extraction techniques, exploring the use of deep learning models, and expanding the dataset to include more diverse types of URLs.

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Secured Data Sharing in Groups Using Attribute-Based Broadcast Encryption in Hybrid Cloud

Cited by 2 publications

References 10 publications

Flexible and secure continues data transmission among multiple users in cloud environment

Flexible and secure continues data transmission among multiple users in cloud environment

Viable Detection of URL Phishing using Machine Learning Approach

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