Titlebook: Homomorphic Encryption and Applications; Xun Yi,Russell Paulet,Elisa Bertino Book 2014 Xun Yi, Russell Paulet, Elisa Bertino 2014 Critical

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Fully Homomorphic Encryption,The problem of constructing a scheme with these properties remained unsolved until 2009, when Gentry [.] presented his breakthrough result. His scheme allows arbitrary computation on the ciphertexts and it yields the correct result when decrypted. This chapter begins with an introduction of FHE mode

irritation

Nearest Neighbor Queries with Location Privacy,l communication complexity for the mobile user to retrieve a type of k nearest POIs is .(. + .) while the computation complexities of the mobile user and the LBS provider are .(. + .) and .(...), respectively. Compared with existing solutions for kNN queries with location privacy, these solutions ar

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Private Searching on Streaming Data,iques, we describe disjunctive, conjunctive, and complement constructions for private threshold queries based on keyword frequency given by Yi et al. [23]. Combining the basic constructions, we also describe their generic construction for arbitrary private threshold queries based on keyword frequenc

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Introduction,must be decrypted, before it can be read by the recipient. In its earliest form, people have been attempting to conceal certain information that they wanted to keep to their own possession by substituting parts of the information with symbols, numbers, and pictures. Today’s encryption algorithms are

Obvious

Homomorphic Encryption,result which, when decrypted, matches the result of operations performed on the plaintexts. This is a desirable feature in modern communication system architectures. RSA is the first public-key encryption scheme with a homomorphic property. However, for security, RSA has to pad a message with random

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Fully Homomorphic Encryption, operation is possible (usually addition or multiplication in the plaintext space) to be able to manipulate the plaintext by using only the ciphertext. What would really be useful is to be able to utilize both addition and multiplication simultaneously. This would permit more manipulation of the pla

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Nearest Neighbor Queries with Location Privacy,on about the mobile user. In this chapter, we consider k nearest neighbor (kNN) queries where the mobile user queries the location-based service (LBS) provider about k nearest points of interest (POIs) on the basis of his or her current location. We described a solution given by Yi et al. [22] for t