ADAPTABILITY CONCEALMENT CONSERVING POSITION BASED REQUEST OVER ENCRYPTED DATA

Our IPRE plan and ˆso-tree perhaps utilized for investigating records indoors an addicted warp Euclidean span or great-circle size too. Weighted Euclidean length perhaps well-known detect the serious controversy in untold types of data, moment great-circle size may be the radius of two points at first peek of the scope. Benefits of recommended organization: To the breathtaking of our considerate, licensed doesn't lie predicate/predicate-only plan promoting internal line of products. Though our plan perhaps used for separateness preserving dimensional area inquire not beyond this card, it perhaps used in alternative applications too. Experiments on the performance show our privilege would-be unusually vigorous. To transfer good user encounters, the POI explores displaying in the perplex side enjoy being done very expeditiously The LBS laborer isn't groomed to unveil its prized LBS data pointing to the distract. Many LBS users are locomotive users, over as their terminals are sharp phones with defined sources. We tell EPLQ, a decent sap for confidentiality preserving structural drift doubt. Particularly, we disclose that even if a POI matches a geographical line interrogate or alternative wise mayhap certified on analyzing if the intrinsic product of two vectors is in reach confirmed differ. Within this report, we watch the recent framework. Within the departed framework, qualifier’s an LBS lord and master land a contiguous index of POI records in ASCII, and LBS users doubt POIs in the Goodman’s site. The LBS jobholder has bountiful of LBS data that are POI records

Security and Privacy for Big Data: A Systematic Literature Review

Big data is currently a hot research topic, with four million hits on Google scholar in October 2016. One reason for the popularity of big data research is the knowledge that can be extracted from analyzing these large data sets. However, data can contain sensitive information, and data must therefore be sufficiently protected as it is stored and processed. Furthermore, it might also be required to provide meaningful, proven, privacy guarantees if the data can be linked to individuals. To the best of our knowledge, there exists no systematic overview of the overlap between big data and the area of security and privacy. Consequently, this review aims to explore security and privacy research within big data, by outlining and providing structure to what research currently exists. Moreover, we investigate which papers connect security and privacy with big data, and which categories these papers cover. Ultimately, is security and privacy research for big data different from the rest of the research within the security and privacy domain? To answer these questions, we perform a systematic literature review (SLR), where we collect recent papers from top conferences, and categorize them in order to provide an overview of the security and privacy topics present within the context of big data. Within each category we also present a qualitative analysis of papers representative for that specific area. Furthermore, we explore and visualize the relationship between the categories. Thus, the objective of this review is to provide a snapshot of the current state of security and privacy research for big data, and to discover where further research is required

Practical and Secure Circular Range Search on Private Spatial Data

With the location-based services (LBS) booming, the volume of spatial data inevitably explodes. In order to reduce local storage and computational overhead, users tend to outsource data and initiate queries to the cloud. However, sensitive data or queries may be compromised if cloud server has access to raw data and plaintext token. To cope with this problem, searchable encryption for geometric range is applied. Geometric range search has wide applications in many scenarios, especially the circular range search. In this paper, a practical and secure circular range search scheme (PSCS) is proposed to support searching for spatial data in a circular range. With our scheme, a semi-honest cloud server will return data for a given circular range correctly without uncovering index privacy or query privacy. We propose a polynomial split algorithm which can decompose the inner product calculation neatly. Then, we define the security of our PSCS formally and prove that it is secure under same-closeness-pattern chosen-plaintext attacks (CLS-CPA) in theory. In addition, we demonstrate the efficiency and accuracy through analysis and experiments compared with existing schemes

A New Array Search On Encrypted Spatial Records

Accessible encryption is a procedure to perform significant questions on encoded information without uncovering protection. Be that as it may, geometric range look on spatial information has not been completely examined nor bolstered by existing accessible encryption plans. In this we plan a symmetric-key accessible encryption conspire that can bolster geometric range inquiries on encoded spatial information. One of our real commitments is that our outline is a general approach, which can bolster diverse sorts of geometric range questions. At the end of the day, our outline on encrypted information is free from the states of geometric range questions. In addition, we additionally expand our plan with the extra utilization of tree structures to accomplish look multifaceted nature that is speedier than linear

Efficient tree structured algorithm for providing confidentiality of location data to minimize communication overhead in LBS Services

We present an effective and protection safeguarding polygons spatial inquiry structure for area based administrations, called Polaris. With Polaris, the LBS supplier redistributes the encoded LBS information to cloud server, and the enrolled client can question any polygon range to get precise LBS results without revealing his/her inquiry data to the LBS supplier and cloud server. Proficient uncommon polygons spatial inquiry calculation over ciphertext is developed dependent on an enhanced homomorphic encryption innovation over Composite request gathering. With SPSQ, Polaris can look re-appropriated scrambled LBS information in cloud server by the encoded demand, and react the scrambled polygons spatial question results precisely

STC database for SQL Range Queries digital apps with Privacy Preserving

Businesses and people outsource database to realize helpful and low-cost applications and administrations. In arrange to supply adequate usefulness for SQL inquiries, numerous secure database plans have been proposed. In any case, such plans are helpless to protection leakage to cloud server. The most reason is that database is facilitated and handled in cloud server, which is past the control of information proprietors. For the numerical extend inquiry (“\u3e”, “\u3c”, etc.), those plans cannot give adequate protection security against viable challenges, e.g., security spillage of measurable properties, get to design. Besides, expanded number of questions will definitely spill more data to the cloud server. In this paper, we propose a two-cloud engineering for secure database, with a arrangement of crossing point conventions that give security conservation to different numeric-related extend questions. Security analysis shows that privacy of numerical information is strongly protected against cloud providers in our proposed scheme

Iris Recognition Approach for Preserving Privacy in Cloud Computing

Biometric identification systems involve securing biometric traits by encrypting them using an encryption algorithm and storing them in the cloud. In recent decades, iris recognition schemes have been considered one of the most effective biometric models for identifying humans based on iris texture, due to their relevance and distinctiveness. The proposed system focuses on encrypting biometric traits. The user’s iris feature vector is encrypted and stored in the cloud. During the matching process, the user’s iris feature vector is compared with the one stored in the cloud. If it meets the threshold conditions, the user is authenticated. Iris identification in cloud computing involves several steps. First, the iris image is pre-processed to remove noise using the Hough transform. Then, the pixel values are normalized, Gabor filters are applied to extract iris features. The features are then encrypted using the AES 128-bit algorithm. Finally, the features of the test image are matched with the stored features on the cloud to verify authenticity. The process ensures the privacy and security of the iris data in cloud storage by utilizing encryption and efficient image processing techniques. The matching is performed by setting an appropriate threshold for comparison. Overall, the approach offers a significant level of safety, effectiveness, and accuracy