The E-Health Cloud Platform Now Supports A Keyword Search Related To Timer Use And Lab-Enabled Proxy Recoding

The delivery of healthcare may be vastly enhanced by the introduction of novel software, such as an electronic health record system. Users' fundamental concerns about the privacy and security of their personal information may be slowing the systems' widespread adoption. The searchable encryption (SE) method is a promising option for the electronic health record system due to its ability to provide strong security without sacrificing usability. Our research introduces a new cryptographic primitive, which we've termed "Re-dtPECK." It's a time-dependent SE approach that combines conjunctive keyword search with a designated tester and a proxy reencryption function that takes time into consideration. Patients may use this function to provide access to their data to carefully chosen researchers for a short period of time. Any allotted period for a delegatee to view and decode their delegator's encrypted papers may be extended if required. It's possible that the delegate's access and search capabilities will expire after a certain period of time has passed. It's also capable of conjunctive keyword searches and resisting assaults based on guessing. Only the authorized tester is allowed to look for the existence of certain keywords in the proposed method. We provide a system model and a security model for the proposed Re-dtPECK approach to prove that it is a safe and effective replacement for the existing standard. Simulations and comparisons with other methods show that it requires very little bandwidth and storage space for data

Improved Hybrid Fingerprint-Based P2P Media Distribution For Privacy Protection

It has been suggested that anonymous fingerprinting could be an easy way to ensure the lawful dissemination of copyright-protected multimedia content without compromising the privacy of customers, whose names would only be revealed in the event of illegal re-distribution of the content. This idea has been put forward as a potential solution to the problem. However, the majority of the currently available anonymous fingerprinting systems are not practical. This is due to the fact that they make use of complicated protocols that take up a lot of time, as well as homomorphic encryption of the data. Furthermore, they distribute the data using a unicast approach, which does not scale well for a large number of clients. The concept of recombined fingerprints serves as the foundation for this body of work, which also makes an effort to overcome some of these restrictions. On the other hand, recommended fingerprint approaches need a complex graph search for traitor monitoring, which in turn demands the participation of additional buyers and honest proxies in their P2P distribution scenario. Getting rid of these issues and developing a fingerprinting system that is not only efficient but also scalable, private, and makes use of P2P technology is the purpose of this research

Secure Clouds Through Reputation-Based Cloud Service Trust Management

Inadequate mechanisms for managing user trust in cloud services are a major roadblock to the broad adoption of this technology. Difficulties with privacy, security, and availability are inevitable in the cloud because of the service's intrinsic malleability, dispersion, and lack of transparency. Due to the sensitive nature of the information shared between customers and the trust management service, confidentiality must be maintained at all times. It's difficult to prevent malicious individuals from disrupting cloud services (for example, by providing false or misleading feedback to make a cloud service seem bad). Due to the dynamic nature of cloud infrastructure, it may be challenging to guarantee the constant availability of the trust management service in a cloud environment. We discuss the design and implementation of Cloud Armor, a reputation-based trust management framework that offers a collection of functions to provide Trust as a Service, with the goals of protecting cloud services from malicious users and comparing the trustworthiness of various cloud services. A unique protocol to verify the credibility of trust feedbacks while protecting users' anonymity; and (ii) an adaptive and resilient credibility model for gauging the veracity of trust feedbacks. Our approach's benefits and viability have been demonstrated through prototype development and experimentation with real-world trust feedback on cloud services

Cloud Storage That Makes Use Of A Feature-Based Encoding Hierarchy To Maximize Efficiency

Sharing data securely in the cloud is a major difficulty, but cipher text-policy attribute-based encryption has emerged as a top tool for meeting this need. The shared data files used in many different professions, including medicine and the military, have a multi-tiered, intricate structure. The file-sharing structure, however, has not been studied in cipher text-policy attribute-based encryption. Here, we provide a novel cloud-based encryption approach that takes advantage of hierarchies of file attributes. Before encrypting a folder tree, it is common practice to merge the various access controls into a single control scheme. Some components of the encryption text that pertain to attributes might be reused between files. The time and money needed to store encrypted documents and conduct encryption are therefore minimized. Finally, it is demonstrated that the proposed method is safe under the null hypothesis. In experimental simulations of encryption and decryption, the proposed method has been proven to be exceedingly efficient. Our method's advantages become more evident as more data is included

Filter-Based Product Search Engines With Dynamic Component Ranking

The use of faceted browsing is common on shopping and comparison websites. When dealing with problems of this kind, it is usual practise to apply a specified set of features in a certain order. This tactic suffers from two major flaws that undermine its effectiveness. First things first: before you do anything else, you need to make sure that you set aside a significant amount of time to compile an effective list. Second, if you have a certain number of aspects and all of the products that are relevant to your search are tagged with the same aspect, then that particular aspect is basically worthless. This article presents a method for doing online business that makes use of a dynamic facet ordering system. On the basis of measurements for specificity and dispersion of aspect value dispersion, the entirely automated system assigns ratings to the characteristics and facets that lead to a speedy drill-down for each and every prospective target product. In contrast to the methodologies that are currently in use, the framework takes into consideration the subtleties that are specific to e-commerce. These nuances include the need for several clicks, the grouping of facets according to the traits that they share, and the predominance of numerical facets. In a large-scale simulation and user survey, our approach performed much better than the baseline greedy strategy, the facet list prepared by domain experts, and the state-of-the-art entropy-based solution. These comparisons were made using the same data

Effective Cloud-Based Strategies For Managing Online Reputations

Leasing computing resources are now feasible thanks to the Infrastructure as a Service (IaaS) concept made available by cloud computing. In spite of the fact that leased computing resources provide a more financially advantageous answer to the requirements of virtual networks, customers are reluctant to make use of them due to low levels of trust in these resources. Multi-tenancy is a method for reducing operating expenses by allocating a single set of computer resources to serve the needs of several users simultaneously. The fact that computer resources and communication methods are being shared gives rise to concerns over the security and integrity of the data. Since the users are anonymous, it may be difficult for a person to decide who among their neighbours can be trusted. This may make it difficult for an individual to choose a place to live. It is very necessary to have faith in the capacity of the cloud provider (CP) to match customers with dependable co-tenants. Yet, it is in the CP's best interest to make the most of the usage of the resources. So, it enables the maximum possible degree of co-tenancy, which is unaffected by the actions of the user. We provide a powerful reputation management system that pays CPs for discriminating between genuine and malicious users. This prevents resource sharing across CPs in a federated cloud environment, which is one of the goals of our system. Through a combination of theoretical and empirical research, we demonstrate that the proposed method for managing reputations is effective and legitimate

Cloud-Based RDF Data Management That'S Both Powerful And Extensible

Even if there have been some recent improvements in the administration of distributed RDF data, it is still rather difficult to do analysis on large amounts of RDF data using the cloud. Although having a very easy data paradigm, RDF is capable of storing complex graphs that mix information at the instance-level and the schema-level. The distributed operations that are produced as a consequence of sharding this sort of data using standard approaches, such as partitioning the graph using usual min-cut algorithms, are exceedingly inefficient and call for a number of joins to be performed. In this paper, we explore DC, a cloud-optimized distributed RDF data management system that is both effective and scalable. It was created primarily for use in cloud environments. In contrast to more conventional approaches, DC first does a physiological analysis on both the instance data and the schema data before it divides the data. In this paper, we provide an overview of the architecture of DC, covering its fundamental data structures as well as the innovative approaches that we use for the division and distribution of data. In addition to this, we provide a comprehensive analysis of DC, which demonstrates that, for the vast majority of workloads, our system is often twice as fast as the most modern alternatives

Identifying Unauthorized Transactions On Credit Cards By Using Machine Learning Methodologies

It is essential for organizations that issue credit cards to be able to recognize fraudulent credit card transactions. This will prevent consumers from being charged for products that they did not buy with their credit card. The purpose of this project is to demonstrate the modelling of a data set via use of machine learning for the detection of credit card fraud. The problem of detecting fraudulent use of credit cards requires modelling previously completed credit card transactions using the information from those that were determined to be fraudulent. After that, this model is put to use to determine whether or not a new transaction constitutes fraudulent activity. Our goal is to appropriately handle misclassified categories by reducing the number of false Negative cases. During this stage of the process, our primary focuses have been on the analysis and preprocessing of data sets, as well as the application of multiple anomaly detection algorithms these algorithms include the local outlier factor and the isolation forest algorithm. We have used IEEE_CIS Fraud dataset, provided by the kaggle .we applied feature extraction technique to reduce the dimensionality of large dataset by extracting only those principle components with highest variance. Given the class imbalance ratio, we measured the accuracy using the Area Under the Precision-Recall Curve (AUPRC) which gives better results than any other previously used models

Asserting The Security Restrictions Applicable To Images Posted By Users To Information Platforms

It's becoming more difficult to maintain privacy in the age of social media, as seen by the recent rash of high-profile examples in which people have inadvertently released private information online. All of these incidents show why it's crucial to have user access management tools for freely available information. To address this requirement, we propose an Adaptive Privacy Policy Prediction (A3P) system that may provide users with guidance on how to organise their picture privacy settings. Here, we investigate if and how a user's privacy preferences may be revealed via their social network settings, image content, and metadata. Our two-tiered method takes into account the user's prior activity on the site to determine the most fitting privacy options for their future picture uploads. Our method employs a policy prediction algorithm to automatically build a policy for each newly submitted image, taking into consideration users' social qualities, and an image classification framework to find groups of photos that may be associated by similar rules. Rulemaking will evolve over time to accommodate shifting public attitudes towards personal data privacy. We provide the results of a large-scale analysis of more than 6,000 policies, demonstrating that our method achieves prediction accuracy of 93% or better

Internet User Advice Based On Collaborative Screening And Voting

The option to vote on different social issues is a feature that has just recently been added to several social media sites. In the context of this question, there are fresh challenges and opportunities for counsel. In this study, we create a suite of recommender systems (RS) to mine users' social networks and group memberships in order to deliver social voting suggestions. We do this by using matrix factorization (MF) and nearest-neighbor (NN). We show that including information about social networks and group membership significantly improves the accuracy of popularity-based vote suggestions, with the former dominating the latter in NN-based methods. This was demonstrated by using data from social votes cast in the real world in experimental settings. In addition, we find that social and group information is valuable to light users to a greater degree than it is to heavy users. Experimentally, we observed that simple meta-path-based NN models performed better than computationally complicated MF models when it came to proposing hot votes. On the other hand, MF models performed better when it came to mining users' interests for cold votes. In addition, we recommend a hybrid RS, which is a combination of several distinct research strategies, in order to get the greatest possible amount of top-k hits