EncodeORE: Reducing Leakage and Preserving Practicality in Order-Revealing Encryption

Abstract

Order-preserving encryption (OPE) is a cryptographic primitive that preserves the order of plaintexts. In the past few years, many OPE schemes were proposed to solve the problem of executing range queries in encrypted databases. However, OPE leaks some certain information (for example, the order of ciphertext), so it is vulnerable to many attacks. Subsequently, order-revealing encryption (ORE) was proposed by Boneh et al. (Eurocrypt 2015) as a generalization of order-preserving encryption. It breaks through the limitation of the numeric order of OPE plaintext. It implements ciphertext comparison for any specific form of plaintext through a publicly computable comparison function. In this work, we aim to design a new ORE scheme which reduces the leakages and preserves the practicality in terms of ciphertext length and encryption time. We first propose the hybrid model named HybridORE. Then, we propose an improved scheme named EncodeORE which achieves acceptable security and appropriate ciphertext length. They both explore the encode strategy of encoding plaintext into different parts and apply suitable ORE algorithms to each part according to its security characteristics to reduce leakages. Compared with the typical CLWW scheme (FSE 2016) and Lewi-Wu (CCS 2016) in large domain, they have fewer leakages. The experiment shows that the proposed EncodeORE is very practical.

Provably Secure Security-Enhanced Timed-Release Encryption in the Random Oracle Model

“…erefore, designing a TRE that can support the specified arbitrary release time, anonymize the user identity, and prevent the time server from denial-of-service attacks will be a very practical and challenging task in the future. In addition, we will explore the combination of TRE with other cryptographic primitives, such as order-revealing encryption [39], so that more scenarios can have the function of controlling the decryption time.…”

Ferry Node Identification Model for the Security of Mobile Ad Hoc Network

“…In this study, three different simulation scenarios were set up and were compared with the betweenness-based algorithm (referred to as the VC model) [35]. e FIR-based model proposed in this study and the VC model were used for identifying the Input: node set G Output: structural hole constraint coefficient, L ni , of all nodes in G (1) for i ∈ G: //G is the node set of the opportunistic network (2) Calculate k (i) (3) for i ∈ G: (4) Calculate Q (i) (5) for i ∈ G: (6) for j ∈ r(i): //r(i) is the set of all neighboring nodes of node i (7) Calculate P ij (8) for q ∈ (r(i) ∩ r(j)): (9) Calculate P iq and P qj (10) Calculate RC i (11) for i ∈ G: (12) Calculate L i (13) for i ∈ G: (14) Calculate L ni (15) return Ln ALGORITHM 1: Algorithm for calculating the importance of structural holes.…”

“…Ferry nodes moving between different regions connect to different areas. At present, research studies on ferry nodes mainly include routing algorithms based on ferry nodes [3][4][5][6][7][8][9][10][11][12], ferry node motion path planning [13][14][15][16][17][18][19], and ferry node network signal coverage [20][21][22][23].…”

An Unsupervised Learning-Based Network Threat Situation Assessment Model for Internet of Things

“…e IoT devices that are attacked by the network may have the management rights of the database stolen. To ensure the privacy and security of the database, the authors of [5,6] proposed encryption algorithms to prevent the leakage of important information. However, in the face of a large number of complex network attacks, it is necessary to ensure network information security from a more comprehensive perspective.…”