Running Big Data Privacy Preservation in the Hybrid Cloud Platform

Now a day’s cloud computing has been used all over the industry, due to rapid growth in information technology and mobile device technology. It is more important task, user’s data privacy preservation in the cloud environment. Big data platform is collection of sensitive and non-sensitive data. To provide solution of big data security in the cloud environment, organization comes with hybrid cloud approach. There are many small scale industries arising and making business with other organization. Any organization data owner or customers never want to scan or expose their private data by the cloud service provider. To improve security performance, cloud uses data encryption technique on original data in public cloud. Proposed system work is carried out how to improve image data privacy preserving in hybrid cloud. For that we are implementing image encryption algorithm based on Rubik’s cube principle improves the image cryptography for the public cloud data securit

A Novel Estimation of Range Queries over Spatial information by Users

We solemnize the idea of Geometrically Searchable Encryption (GSE), which is changed from the definitions of SE arrangements but focuses on replying geometric queries. We suggest a GSE scheme, named FastGeo, which can efficiently save points inside a geometric area deprived of skimpy private data points or subtle geometric range queries to a honest-but inquisitive server. In its place of straight assessing calculate then-compare operations, our key idea is to change spatial data and regular range queries to a newfangled form, signified as equality-vector form, and influence a two-level search as our key solution to prove whether a point is secret a geometric range, where the first level firmly operates equivalence scrutiny with PRF and the next level clandestinely evaluates inner products with Shen-Shi-Waters encryption (SSW). FastGeo provisions uninformed geometric areas, reaches sub linear search time, and aids energetic updates over converted longitudinal datasets

A New Evaluation of Range Queries over Spatial Data by Clients

Propose a proficient plan, named FastGeo, to ensure the protection of customers' spatial datasets put away and questioned at an open server. With FastGeo, which is a novel two-level scan for encoded spatial information, a legitimate yet inquisitive server can proficiently perform geometric range questions, and accurately return information focuses that are inside a geometric range to a customer without learning delicate information focuses or this private inquiry. FastGeo bolsters subjective geometric territories, accomplishes sub straight pursuit time, and empowers dynamic updates over encoded spatial datasets. Our plan is provably secure

Efficient evaluation of range queries over spatial data by clients

The notion of Geometrically Searchable Encryption, and anticipated an effectual scheme, named FastGeo, to keep the privacy of clients’ spatial datasets kept and enquired at a public server. With FastGeo, which is a novel two-level search for scrambled spatial data, an honest-but-curious server can proficiently complete geometric range queries, and suitably return data points that are private a geometric range to a client without culture penetrating data points or this cloistered query. FastGeo wires arbitrary geometric areas, accomplishes sub linear search time, and qualifies go-ahead updates over encrypted spatial datasets. Our outline is provably sheltered, and our new results on real-world spatial datasets in cloud platform determine that FastGeo can enhance search time over 100 times

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

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