Private Searching for Single and Conjunctive Keywords on Streaming Data

ABSTRACT Private searching on streaming data allows a user to collect potentially useful information from huge streaming sources of data without revealing his or her searching criteria. This technique can be used for airports, without knowing a classified "possible terrorists" list, to find if any of hundreds of passenger lists has a name from the "possible terrorists" list and if so his/hers itinerary. Current solutions for private searching on streaming data only support searching for "OR" of keywords or "AND" of two sets of keywords. In this paper, we extend the types of private queries to support searching on streaming data for an "OR" of a set of both single and conjunctive keywords, such as are single keywords and , , … , , are unordered conjunctive keywords. Our protocol is built on Boneh et al.'s result for the evaluation of 2-DNF formulas on ciphertexts. The size of our encrypted dictionary is | | only, which is much less than | | , the size of the encrypted dictionary if conjunctive keywords , ( = 1,2, … , ) is treated as single keyword, where we assume , ∈ ( = 1,2, … , )

Confidential Information-Sharing for Automated Sustainability Benchmarks

The pressure on enterprises to manage and improve their environmental sustainability is steadily increasing. Despite the growing awareness in the IS community and business practice, current IS solutions remain in an initial state. Sustainability benchmarking is seen as a novel and effective tool in this context. However, sustainability benchmarking faces two major obstacles: First, the heterogeneity of the data requires significant pre-processing, and, second, the sensitivity of the data causes enterprises to reluctantly share this data. Our contribution is twofold: After analyzing the data input problem and identifying appropriate and available solutions, we present a secure sustainability benchmarking service (SBS) to overcome the information-sharing problem. Our service uses homomorphic encryption to protect the data during processing and differential privacy to protect against leakages from the reports. Finally, we evaluate in detail a prototypical implementation of this secure sustainability benchmarking service and illustrate its applicability in industry

Enhanced fully homomorphic encryption scheme using modified key generation for cloud environment

Fully homomorphic encryption (FHE) is a special class of encryption that allows performing unlimited mathematical operations on encrypted data without decrypting it. There are symmetric and asymmetric FHE schemes. The symmetric schemes suffer from the semantically security property and need more performance improvements. While asymmetric schemes are semantically secure however, they pose two implicit problems. The first problem is related to the size of key and ciphertext and the second problem is the efficiency of the schemes. This study aims to reduce the execution time of the symmetric FHE scheme by enhancing the key generation algorithm using the Pick-Test method. As such, the Binary Learning with Error lattice is used to solve the key and ciphertext size problems of the asymmetric FHE scheme. The combination of enhanced symmetric and asymmetric algorithms is used to construct a multi-party protocol that allows many users to access and manipulate the data in the cloud environment. The Pick-Test method of the Sym-Key algorithm calculates the matrix inverse and determinant in one instance requires only n-1 extra multiplication for the calculation of determinant which takes 0(N3) as a total cost, while the Random method in the standard scheme takes 0(N3) to find matrix inverse and 0(N!) to calculate the determinant which results in 0(N4) as a total cost. Furthermore, the implementation results show that the proposed key generation algorithm based on the pick-test method could be used as an alternative to improve the performance of the standard FHE scheme. The secret key in the Binary-LWE FHE scheme is selected from {0,1}n to obtain a minimal key and ciphertext size, while the public key is based on learning with error problem. As a result, the secret key, public key and tensored ciphertext is enhanced from logq , 0(n2log2q) and ((n+1)n2log2q)2log q to n, (n+1)2log q and (n+1)2log q respectively. The Binary-LWE FHE scheme is a secured but noise-based scheme. Hence, the modulus switching technique is used as a noise management technique to scale down the noise from e and c to e/B and c/B respectively thus, the total cost for noise management is enhanced from 0(n3log2q) to 0(n2log q) . The Multi-party protocol is constructed to support the cloud computing on Sym-Key FHE scheme. The asymmetric Binary-LWE FHE scheme is used as a small part of the protocol to verify the access of users to any resource. Hence, the protocol combines both symmetric and asymmetric FHE schemes which have the advantages of efficiency and security. FHE is a new approach with a bright future in cloud computing