Privacy Of Cloud Users Research Articles

Modelling a Request and Response-Based Cryptographic Model For Executing Data Deduplication in the Cloud

Cloud storage is one of the most crucial components of cloud computing because it makes it simpler for users to share and manage their data on the cloud with authorized users. Secure deduplication has attracted much attention in cloud storage because it may remove redundancy from encrypted data to save storage space and communication overhead. Many current safe deduplication systems usually focus on accomplishing the following characteristics regarding security and privacy: Access control, tag consistency, data privacy and defence against various attacks. But as far as we know, none can simultaneously fulfil all four conditions. In this research, we offer a safe deduplication method that is effective and provides user-defined access control to address this flaw. Because it only allows the cloud service provider to grant data access on behalf of data owners, our proposed solution (Request-response-based Elliptic Curve Cryptography) may effectively delete duplicates without compromising the security and privacy of cloud users. A thorough security investigation reveals that our approved safe deduplication solution successfully thwarts brute-force attacks while dependably maintaining tag consistency and data confidentiality. Comprehensive simulations show that our solution surpasses the evaluation in computing, communication, storage overheads, and deduplication efficiency.

Hybrid machine learning approach based intrusion detection in cloud: A metaheuristic assisted model

Cloud computing provides various cost-effective on-demand services to the user and so it is rising like a real trend in the IT service model. However, due to its open and distributed architecture, it is highly vulnerable to attackers. The security and privacy of cloud users has become a major hurdle. The most prevalent approach for detecting attacks on the cloud is the Intrusion Detection System (IDS). Scalability and autonomous self-adaptation weren’t features of contemporary IDS deployed in traditional Internet or Intranet contexts. Furthermore, they lack determinism, making them inappropriate for cloud-based settings. This necessitates the development of new cloud-based IDS capable of fulfilling the firm’s security standards. Therefore, in this research work, we have introduced a new IDS model for the cloud environment. Our research work is made up of four major phases: “data pre-processing, optimal clustering, feature selection, and attack detection phase”. Initially, the collected raw data are pre-processed to enhance the quality of the data. Then, these pre-processed data are segmented with the newly introduced K-means clustering model, where we’ve optimally selected the centroids by introducing a new hybrid optimization model referred as Spider Monkey Updated with Sealion Optimization (SMSLO), which is the conceptual hybridization of standard SeaLion Optimization (SLnO) and Spider Monkey Optimization (SMO), respectively. At the end of segmentation, two clusters (attack data and non-attack data) will be formed. The data available in both clusters seems to be huge in dimensions, so we’ve lessened the dimensions of the data in the clusters by applying the “Principal Component Analysis (PCA)” algorithm. Subsequently, these dimensionality-reduced features pass into the attack detection phase. The attack detection phase is modeled with the optimized Deep Belief Network (DBN), which portrays the type of attack (Dos, Botnet, DDoS as well) that intruded into the network. Since the DBN makes the final detections; it is ought to be less prone to errors. We have lessened the detection errors such as the Mean Square Error (MSE) of DBN by fine-tuning its weight using a new hybrid optimization model (SMSLO). Finally, the result acquired from the proposed work (DBN+SMSLO) is validated.

Review on Cloud Security and Challenges on Higher Education

Many new developments are often plagued with security concerns. Although emerging innovations have all the potential to change systems and people's lives, security issues hinder them adoption and usage. The transformations of data management brought by cloud technologies have helped both individuals and companies. While the cloud allows data storage, connectivity, and recovery stability, it is not immune to the security threats that other new technologies pose. Concerns have been presented over the protection and privacy of cloud users' data. Data leakage, data manipulation, condemnation, a Lack of Services assault, impersonating personalities, also unauthorised access to data are all examples of activities which can appear while data is outsourced to a cloud. Individuals and organisations will orchestrate these attacks, which will jeopardise the reliability and security of cloud networks. The vast majority of colleges and universities are digitising their records, which are then stored in the cloud and handled by a third party. Despite the sacred importance of educational information, the study explores the impact of cloud security problems on schooling. According to the findings, security is crucial for the secure migration and application of cloud technology in the educational sector. It also shows that the growing security threats associated with cloud technology can prevent many education institutions from using cloud services.
 
 Keywords: Cloud technology, Cloud Security, Educational, Information leakage, Privacy

Privacy Enhanced Cloud-Based Facial Recognition

Homomorphic encryption is a significant method to protect user privacy in cloud computing environment. Due to the computation efficiency issue, there is still not many homomorphic encryption applications for common users. In this paper,we try to use homomorphic encryption to enhance the privacy in cloud-based face recognition system. By balancing the workload between client and server,and reimplementing the similarity measurement function, our homomorphic encryption version’s performance is almost the same as the original version in terms of accuracy and time consumption. Our work is especially beneficial to many face recognition methods that are using Euclidian distance as their similarity metric.

RETRACTED ARTICLE: Protecting user profile based on attribute-based encryption using multilevel access security by restricting unauthorization in the cloud environment

Data security in centralized storage needs advancement in privacy standards because of all of the cloud. The data security in the cloud has been well provided by security industries like cloud network security, data center security, distributed security and soon, also their exist numerous techniques to preserve the privacy of cloud users. The earlier methods enforce user privacy by restricting the malicious access from various users. Data authentication is provable access to keep privacy among other standards. However, the privacy of cloud users has been breached on several occasions. To improve cloud security and enforce efficient privacy preservation, a multi-level micro access restriction algorithm has been presented in this paper. The cloud data has been indexed in multiple levels, the data present in each level has been restricted using the profile and set if encryption standards. The user request has been evaluated for its trusted access according to the access grant present in profile data. Similarly, the cloud data has been encrypted with the user key and the key belongs to the data owner. The method estimates micro access trust weight (MATW), which has been used to restrict the user from malicious access and to preserve user privacy. The method improves the performance of cloud security and introduces higher privacy preservation accuracy.

Achieving Efficient Secure Deduplication With User-Defined Access Control in Cloud

Cloud storage as one of the most important services of cloud computing which significantly facilitates cloud users to outsource their data to the cloud for storage and share them with authorized users. In cloud storage, secure deduplication has been widely investigated as it can eliminate the redundancy over the encrypted data to reduce storage space and communication overhead. Regarding the security and privacy, many existing secure deduplication schemes generally focus on achieving the following properties: data confidentiality, tag consistency, access control, and resistance to brute-force attacks. However, as far as we know, none of them can achieve these four requirements at the same time. To overcome this shortcoming, in this article, we propose an efficient secure deduplication scheme that supports user-defined access control. Specifically, by allowing only the cloud service provider to authorize data access on behalf of data owners, our scheme can maximally eliminate duplicates without violating the security and privacy of cloud users. Detailed security analysis shows that our authorized secure deduplication scheme achieves data confidentiality and tag consistency while resisting brute-force attacks. Furthermore, extensive simulations demonstrate that our scheme outperforms the existing competing schemes, in terms of computational, communication and storage overheads as well as the effectiveness of deduplication.

Heterogeneous Data Storage Management with Deduplication in Cloud Computing

Cloud storage as one of the most important services of cloud computing helps cloud users break the bottleneck of restricted resources and expand their storage without upgrading their devices. In order to guarantee the security and privacy of cloud users, data are always outsourced in an encrypted form. However, encrypted data could incur much waste of cloud storage and complicate data sharing among authorized users. We are still facing challenges on encrypted data storage and management with deduplication. Traditional deduplication schemes always focus on specific application scenarios, in which the deduplication is completely controlled by either data owners or cloud servers. They cannot flexibly satisfy various demands of data owners according to the level of data sensitivity. In this paper, we propose a heterogeneous data storage management scheme, which flexibly offers both deduplication management and access control at the same time across multiple Cloud Service Providers (CSPs). We evaluate its performance with security analysis, comparison and implementation. The results show its security, effectiveness and efficiency towards potential practical usage.