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Location-based services (LBS) require users to continuously report their location to a potentially untrusted server to obtain services based on their location, which can expose them to privacy risks. Unfortunately, existing privacy-preserving techniques for LBS have several limitations, such as requiring a fully-trusted third party, offering limited privacy guarantees and incurring high communication overhead. . (1) The system only requires a semi-trusted third party, responsible for carrying out simple matching operations correctly. This semi-trusted third party does not have any information about a user’s location. (2) Secure snapshot and continuous location privacy is guaranteed under our defined adversary models. (3) The communication cost for the user does not depend on the user’s desired privacy level, it only depends on the number of relevant points of interest in the vicinity of the user. (4) Although we only focus on range and k-nearest-neighbor queries. In this work, our system can be easily extended to support other spatial queries without changing the algorithms run by the semi-trusted third party and the database server, provided the required search area of a spatial query can be abstracted into spatial regions. Experimental results show that our DGS is more efficient than the state-of-the-art privacy-preserving technique for continuous LBS.
TA is the attribute authority center, which is responsible for the initialization of system parameters, and the generation of attribute private keys and trapdoor.
It a participant that supplies outsourcing computing service for TA by completing the costly key generation tasks allocated by TA.
It is a participant that supplies outsourcing computing service through accomplishing partial decryption for cipher texts and keyword search service on the partially decrypted cipher texts for data users who want to access the cipher text.
It is a participant that supplies out sourcing data storage service for users who want to share file in cloud.
This is a participant who intends to upload and share his data files on the cloud storage system in a secure way. The encrypted cipher texts will be shared with intended receivers whose access structure will be satisfied by attribute set embedded in cipher texts, that is to say the predicate. The responsibility of DO is to generate indexes for some keywords and upload encrypted data with the indexes
This is a participant who decrypts the encrypted data stored in S-CSP with the help of D-CSP. If the attribute set for DU satisfies the access structures, DU is able to access the encrypted files and recover the original files from it. DU downloads intended cipher texts with the help of trapdoor associated with appointed keyword. Data user is responsible for choosing keywords to create trapdoor, and decrypting data.
In this setting the goal is to permit accurate data mining models to be built over aggregates while preventing disclosure of individual items. Here the published viewis the result of applying a randomization operator to data values or a distribution of datavalues. It is shown that a known information-theoretic definition of privacy may permitcertain disclosures, and they propose an extended measure to account for this drawback.A protection mechanism for relational databases was proposed in based on a probabilistic definition of security similar to our own. An algorithm for deciding query-view security was not known, and relative security and quantified security were not addressed. In the same authors focus on comparing probabilistic independence (closely related to our security criterion) with algebraic independence, but are not concerned with a decision procedure for probabilistic independence.
To addresses the problem of determining what data can safely be published. Theanswer to this question is a critical prerequisite to forming a policy for permitting or denying access, and requires a precise understanding of the information disclosure that may result from publishing data. Analyzing disclosure is difficult because confidential facts may be unexpectedly inferred from published data, and also because authorized recipients may collude to make unauthorized discoveries. Our analysis of disclosure is applied to data stored in relational form, prior to publishing. In such systems, the basic object of protection is the relational view. To formulate the disclosure problem, we assume the data owner has a sensitive query, the answer to which must remain secret. We propose and analyze a new theoretical standard for determining when it is safe to publish the view without disclosing information about a sensitive query.
Cloud computing is defined as the set of resources or services offered through the internet to the users on their demand by cloud providers. As each and every organization is moving its data to the cloud, means it uses the storage service provided by the cloud provider. So there is a need to protect that data against unauthorized access, modification or denial of services etc. As our proposed algorithm is a Multilevel Encryption and Decryption algorithm. Thus, in our proposed work, only the authorized user can access the data. Even if some intruder (unauthorized user) gets the data accidentally or intentionally, he must have to decrypt the data at each level which is a very difficult task without a valid key. It is expected that using multilevel encryption will provide more security for Cloud Storage than using single level encryption.