A new method for secure kNN algorithm to guarantee the security of the outsourced data maintaining its searchability

Distributed computing is a promising IT framework that can make a ton out of IT resources in a profitable and versatile manner. Continuously different associations plan to move their close by information the board systems to the cloud and store and manage their item information on cloud servers. A going with challenge is the way to guarantee the security of the monetarily mystery information while keeping up the capacity to look through the information. In this paper, a security protecting information search plan is suggested that can support both the identifier-based and include based item look. Specifically, two novel rundown trees are created and encoded that can be looked without knowing the plaintext information

A novel hybrid mechanism for credit card fraud detection on financial data

Credit card fraud is a difficult issue in budgetary services. Billions of dollars are lost because of credit card fraud consistently. There is an absence of research thinks about on investigating genuine Master card data inferable from secrecy issues. In this project, machine learning algorithms are used to recognize credit card fraud. Standard models are right off the bat used. At that point, half and half techniques which use AdaBoost and larger part casting a voting method are connected. To assess the model viability, a freely credit card data collection is used. Then, a real-world credit card data set from a financial institution is analyzed

Enhanced method to detect spammers in social network

We present a hybrid methodology for recognizing computerized spammers by amalgamating network based highlights with other element classifications, specifically metadata-, content-, and collaboration based highlights. The curiosity of the proposed methodology lies in the portrayal of clients dependent on their associations with their devotees given that a client can sidestep includes that are identified with his/her own exercises, however sidestepping those dependent on the adherents is troublesome. Nineteen different features, including six newly defined features and two redefined features, are identified for learning three classifiers, namely, random forest, decision tree, and Bayesian network, on a real dataset that comprises benign users and spammers. The separation intensity of various element classes is likewise broke down, and association and network based highlights are resolved to be the best for spam recognition, though metadata-based highlights are ended up being to be the least viabl

Efficient User-Question Mapping Technique For Relevant Answer Retrieval

SocialQ&A powers the social network possessions of common-interest and mutual-trust friend association to classify an asker through friendship that are most probable to answer the question, and improve the user security. We also recover SocialQ&A with safety and competence improvements by caring user discretion and recognizes, and saving answers routinely for regular questions. We define the construction and procedures, and showed complete large-scale imitation to assess SocialQ&A in judgment with other methods. Our consequences propose that social networks can be leveraged to recover the answer quality and asker’s waiting time. We also applied actual prototype of SocialQ&A, and examine the Q&A conduct of real users and questions from a small-scale real-world SocialQ&A scheme

A new architecture and energy efficient keyword search in cloud

Normally with limited bandwidth capacity and limited battery life, these issues acquaint substantial overhead with registering and correspondence just as a higher power utilization for cell phone clients, which makes the encrypted search over mobile cloud very testing. In this paper, we propose TEES (Traffic and Energy saving Encrypted Search), a data transfer capacity and vitality effective encoded search design over mobile cloud. The proposed design offloads the calculation from cell phones to the cloud, and we further improve the correspondence between the versatile customers and the cloud. It is exhibited that the data security does not corrupt when the execution upgrade strategies are connected

Fine-grained access control and data confidentiality in public cloud

We initially propose an adaptable structure where user can utilize his property estimations and a hunt search to locally determine pursuit ability, and a record can be recovered just when its keywords coordinate the query and the user's trait esteems can pass the approach check. Utilizing this structure, we propose a novel plan called Keyword Search with Access Control (KSAC), which empowers keyword seek with access power over encoded information. Keyword Search with Access Control (KSAC) uses ongoing cryptographic crude called various leveled predicate encryption to implement fine-grained access control and perform multi-field query search. In the interim, it likewise bolsters the hunt capacity deviation, and accomplishes productive access arrangement update just as catchphrase update without bargaining information security. To improve the protection, Keyword Search With Access Control (KSAC) likewise plants commotions in the search to conceal users' entrance benefits. Serious assessments on genuine world dataset are directed to approve the appropriateness of the proposed plan and exhibit its insurance for user's entrance benefit

Passmatrix: efficient authentication mechanism to protect user data

We present a safe graphical verification arrangement named Pass Matrix that defends users from flattering fatalities of bear surfing attacks when entering passwords in community through the custom of one-time login indicators. A login indicator is arbitrarily made for each pass-image and will be unusable after the meeting dismisses. The login pointer delivers better safety in contradiction of shoulder surfing attacks, meanwhile users use an active pointer to point out the position of their passwords slightly than snapping `on the key thing straight