Shared Data Files Research Articles

From Trend to Choices: Cognitive Dissonance and Fast-Fashion Consumption Among Indonesian Influencers

In 2010, Instagram was introduced as a free photo-sharing app for iOS and Android users, providing a unique platform for sharing life experiences through visual content. This study, spanning five months (July-December 2023), explores the rise of fast fashion consumption and influencer trends in Indonesia. It employs the diffusion model proposed by Beaudoin, Moore, and Goldsmith. Analysis of interview data using NVivo highlights a strong link between the fashion industry and influencers. NVivo enables remote collaboration, allowing individuals from different locations to work on shared data files in real-time. The study emphasizes data collection through structured interviews based on an in-depth examination of influencers. Findings reveal that the high-value nature of fast fashion significantly influences Indonesian influencers, driven by the growing popularity of consumer trends and ongoing style improvements in the fashion industry. However, it emphasizes the importance of recognizing that unquestionably following trends can profoundly shape an individual's lifestyle, echoing previous research on the pivotal role of trends in fashion choices. Overall, the research underscores the substantial impact of trends and influencers on Indonesia's fast fashion landscape.

Efficient Multi-authority Access Control using Attribute-based Encryption in Cloud Storage

Cloud users are outsourcing their data day-by-day resulting in huge amount of data storage in Cloud server. Cloud service provider (CSP) themselves may go for modifying the data that leads to data integrity problem. Unauthorized users illegally gain access to the data stored in cloud server. Therefore, providing protection, achieving privacy and access control for user’s data are big challenges in the Cloud. Ciphertext-policy attribute-based encryption (CP-ABE) is prominent encryption technique to solve the exigent dilemma of secure data sharing in cloud computing. The shared data files generally have the characteristic of multilevel hierarchy, predominantly in the areas of IT companies, healthcare and military; these areas require a multi-authority access control. However, the hierarchy construction of shared files and multi-authority access control approach are not been investigated in CP-ABE. This paper proposes an efficient multi-authority access control using attribute-based encryption scheme that achieves efficient, fine grained access control. The proposed scheme consists of one algorithm – (Role Hierarchy Algorithm (RHA) and Hierarchy Access Structure (HAS) to protect user’s data, achieve privacy, multi-authority access control and fine grained access to a stored data. The RHA algorithm classifies Cloud users into groups based on their assigned attributes. The HAS helps to define an access structure for fine grained and multi-authority access control of Cloud resources. Simulation results prove that the proposed RHA, HAS are efficient (in terms of time consumption and storage consumption for encryption, decryption) compared to existing works. The advantages of proposed scheme become more and more conspicuous when there is more number of files at Cloud storage server.

A Research on Privacy Preserving for Data I Storage in Cloud Center

A cloud computing is an important aspect for transmitting data through internet. Cloud providers provide data through the data centers to the cloud users for the purpose of compute, storage, and network resource demands throughout the world. User of the cloud can utilize the economical advance for data sharing between group members with low maintenance cost. In the meantime, it should provide security assurance for the sharing data files as a result of they are outsourced. unfortunately, due to the regular modification of the membership, distribution of data whereas offering privacypreserving remains to be a difficult issue, significantly for an untrusted cloud because of the collusion harass. Moreover, several schemes were projected, the protection of key allocation is predicated on the protected communication passage, however, to own such passage could be a robust assumption and is hard for observe. In this paper we provide numerous approaches for the secure data storage within the cloud.

A SECURE ANTI-COLLUSION DATA SHARING SCHEME FOR DYNAMIC GROUPS IN THE CLOUD

Benefited from cloud computing, users can achieve an effective and economical approach for data sharing among group members in the cloud with the characters of low maintenance and little management cost. Meanwhile, we must provide security guarantees for the sharing data files since they are outsourced. Unfortunately, because of the frequent change of the membership, sharing data while providing privacy-preserving is still a challenging issue, especially for an untrusted cloud due to the collusion attack. Moreover, for existing schemes, the security of key distribution is based on the secure communication channel, however, to have such channel is a strong assumption and is difficult for practice. In this paper, we propose a secure data sharing scheme for dynamic members. Firstly, we propose a secure way for key distribution without any secure communication channels, and the users can securely obtain their private keys from group manager. Secondly, our scheme can achieve fine-grained access control, any user in the group can use the source in the cloud and revoked users cannot access the cloud again after they are revoked. Thirdly, we can protect the scheme from collusion attack, which means that revoked users cannot get the original data file even if they conspire with the untrusted cloud. In our approach, by leveraging polynomial function, we can achieve a secure user revocation scheme. Finally, our scheme can achieve fine efficiency, which means previous users need not to update their private keys for the situation either a new user joins in the group or a user is revoked from the group.

An Efficient File Hierarchy Attribute-Based Encryption Scheme in Cloud Computing

Ciphertext-policy attribute-based encryption (CP-ABE) has been a preferred encryption technology to solve the challenging problem of secure data sharing in cloud computing. The shared data files generally have the characteristic of multilevel hierarchy, particularly in the area of healthcare and the military. However, the hierarchy structure of shared files has not been explored in CP-ABE. In this paper, an efficient file hierarchy attribute-based encryption scheme is proposed in cloud computing. The layered access structures are integrated into a single access structure, and then, the hierarchical files are encrypted with the integrated access structure. The ciphertext components related to attributes could be shared by the files. Therefore, both ciphertext storage and time cost of encryption are saved. Moreover, the proposed scheme is proved to be secure under the standard assumption. Experimental simulation shows that the proposed scheme is highly efficient in terms of encryption and decryption. With the number of the files increasing, the advantages of our scheme become more and more conspicuous.

Anonymizing popularity in online social networks with full utility

With the rapid growth of social network applications, more and more people are participating in social networks. Privacy protection in online social networks becomes an important issue. The illegal disclosure or improper use of users’ private information will lead to unaccepted or unexpected consequences in people’s lives. In this paper, we concern on authentic popularity disclosure in online social networks. To protect users’ privacy, the social networks need to be anonymized. However, existing anonymization algorithms on social networks may lead to nontrivial utility loss. The reason is that the anonymization process has changed the social network’s structure. The social network’s utility, such as retrieving data files, reading data files, and sharing data files among different users, has decreased. Therefore, it is a challenge to develop an effective anonymization algorithm to protect the privacy of user’s authentic popularity in online social networks without decreasing their utility. In this paper, we first design a hierarchical authorization and capability delegation (HACD) model. Based on this model, we propose a novel utility-based popularity anonymization (UPA) scheme, which integrates proxy re-encryption with keyword search techniques, to tackle this issue. We demonstrate that the proposed scheme can not only protect the users’ authentic popularity privacy, but also keep the full utility of the social network. Extensive experiments on large real-world online social networks confirm the efficacy and efficiency of our scheme.

A Secure Anti-Collusion Data Sharing Scheme for Dynamic Groups in the Cloud

Benefited from cloud computing, users can achieve an effective and economical approach for data sharing among group members in the cloud with the characters of low maintenance and little management cost. Meanwhile, we must provide security guarantees for the sharing data files since they are outsourced. Unfortunately, because of the frequent change of the membership, sharing data while providing privacy-preserving is still a challenging issue, especially for an untrusted cloud due to the collusion attack. Moreover, for existing schemes, the security of key distribution is based on the secure communication channel, however, to have such channel is a strong assumption and is difficult for practice. In this paper, we propose a secure data sharing scheme for dynamic members. First, we propose a secure way for key distribution without any secure communication channels, and the users can securely obtain their private keys from group manager. Second, our scheme can achieve fine-grained access control, any user in the group can use the source in the cloud and revoked users cannot access the cloud again after they are revoked. Third, we can protect the scheme from collusion attack, which means that revoked users cannot get the original data file even if they conspire with the untrusted cloud. In our approach, by leveraging polynomial function, we can achieve a secure user revocation scheme. Finally, our scheme can achieve fine efficiency, which means previous users need not to update their private keys for the situation either a new user joins in the group or a user is revoked from the group.

Confidentiality and spatially explicit data: concerns and challenges.

Recent theoretical, methodological, and technological advances in the spatial sciences create an opportunity for social scientists to address questions about the reciprocal relationship between context (spatial organization, environment, etc.) and individual behavior. This emerging research community has yet to adequately address the new threats to the confidentiality of respondent data in spatially explicit social survey or census data files, however. This paper presents four sometimes conflicting principles for the conduct of ethical and high-quality science using such data: protection of confidentiality, the social-spatial linkage, data sharing, and data preservation. The conflict among these four principles is particularly evident in the display of spatially explicit data through maps combined with the sharing of tabular data files. This paper reviews these two research activities and shows how current practices favor one of the principles over the others and do not satisfactorily resolve the conflict among them. Maps are indispensable for the display of results but also reveal information on the location of respondents and sampling clusters that can then be used in combination with shared data files to identify respondents. The current practice of sharing modified or incomplete data sets or using data enclaves is not ideal for either the advancement of science or the protection of confidentiality. Further basic research and open debate are needed to advance both understanding of and solutions to this dilemma.

Security Implications of Virus Management

Personal computer viruses have existed for several years. Their continual changes and new vectors are a substantial annoyance to many businesses. The increased use of the Internet, reliance on e-mail, and shared data files and attachments, commensurate with the expanded power of macro languages, have generated new problems for companies and security experts.

Optimized caching in systems with heterogeneous client populations

An important question in providing on-demand access to large widely shared data files, such as popular video files, is how to effectively use regional (proxy) servers that can store some of the data close to the clients. The proxy caching problem is more complex in the context of continuous media files because of the need to consider bandwidth as well as storage constraints at the proxy servers, and because of the bandwidth sharing possibilities provided by recently proposed multicast delivery techniques. This paper develops new highly efficient analytic models for determining optimal proxy cache content in such environments. Specifically, the new models apply to heterogeneous systems in which the proxy servers have different client workloads and server capabilities. Results from the models provide general insights into caching strategies for such systems, and suggest that it may be useful to employ efficient cost models in actual systems to determine what content should be cached in response to the measured client workload.

The AppLeS Parameter Sweep Template: User‐Level Middleware for the Grid

The Computational Grid is a promising platform for the efficient execution of parameter sweep applications over large parameter spaces. To achieve performance on the Grid, such applications must be scheduled so that shared data files are strategically placed to maximize re‐use, and so that the application execution can adapt to the deliverable performance potential of target heterogeneous, distributed and shared resources. Parameter sweep applications are an important class of applications and would greatly benefit from the development of Grid middleware that embeds a scheduler for performance and targets Grid resources transparently. In this paper we describe a user‐level Grid middleware project, the AppLeS Parameter Sweep Template (APST), that uses application‐level scheduling techniques [1] and various Grid technologies to allow the efficient deployment of parameter sweep applications over the Grid. We discuss several possible scheduling algorithms and detail our software design. We then describe our current implementation of APST using systems like Globus [2], NetSolve [3] and the Network Weather Service [4], and present experimental results.

The determination of efficient record segmentations and blocking factors for shared data files

It is generally believed that 80 percent of all retrieval from a commercial database is directed at only 20 percent of the stored data items. By partitioning data items into primary and secondary record segments, storing them in physically separate files, and judiciously allocating available buffer space to the two files, it is possible to significantly reduce the average cost of information retrieval from a shared database. An analytic model, based upon knowledge of data item lengths, data access costs, and user retrieval patterns, is developed to assist an analyst with this assignment problem. A computationally tractable design algorithm is presented and results of its application are described.

A dynamic programming approach to dependent task clustering

In Complex Data Management Systems, the program generator must consider segmentation of the data processing program resulting from the generation. There are well-defined breaking points where the only dependency between program segments is the use of external data files. If program segments sharing data files were to be grouped together in the same processing unit, a record constructed for, or read from, a shared file could be routed to each segment using that file, eliminating redundant reading of the file. This paper considers the optimization of clustering program segments into processing units. It is a dynamic or selective trial and error approach which considers constraints of execution order, both implicit and explicit.