Order-revealing Encryption Research Articles

Towards Practical Multi-Client Order-Revealing Encryption: Improvement and Application

Order-revealing encryption (ORE) enables the untrusted server to perform greater-than-comparison over ciphertext without compromising data privacy, which allows anyone to evaluate the lexicographic ordering of two arbitrary ciphertexts with a public comparison algorithm. However, most ORE constructions merely support ciphertext comparison for single-user. Recently, a variant of ORE named delegatable ORE has been introduced, which achieves cross-user ciphertext comparison by employing token mutual authorization technique at the cost of weak security, i.e., reveals the most significant differing bit of underlying plaintexts. To tackle this problem, we first present a deterministic property-preserving hash called DPPH with short-size hash value, and then propose a novel multi-client ORE scheme (m-ORE) from DPPH that supports ciphertext comparison among multiple users while hiding the most significant differing bits. Furthermore, we present an enhanced construction dubbed m-H-ORE by introducing a two-phase comparison method, which can achieve supper-efficient comparison in some cases, i.e., two ciphertexts with different bit-length. Finally, we provide formal security proofs of the proposed schemes and run extensive experiments to evaluate their performance on real-world and synthetic datasets. The results demonstrate that both of the proposed schemes can achieve a speedup of 47× and 138× in comparison cost to that of parameter-hiding ORE, respectively.

A Privacy-Preserving Solution for Intelligent Transportation Systems: Private Driver DNA

The rising connection of vehicles with the road infrastructure enables the creation of data-driven applications to offer drivers customized services. At the same time, these opportunities require innovative solutions to protect the drivers’ privacy in a complex environment like an Intelligent Transportation System (ITS). This need is even more relevant when data are used to retrieve personal behaviors or attitudes. In our work, we propose a privacy-preserving solution, called Private Driver DNA, which designs a possible architecture, allowing drivers of an ITS to receive customized services. The proposed solution is based on the concept of Driver DNA as characterization of driver’s driving style. To assure privacy, we perform the operations directly on sanitized data, using the Order Revealing Encryption (ORE) method. Besides, the proposed solution is integrated with ITS architecture defined in the European project E-Corridor. The result is an effective privacy-preserving architecture for ITS to offer customized products, which can be used to address drivers’ behaviors, for example, to environmental-friendly attitudes or a more safe driving style. We test Private Driver DNA using a synthetic dataset generated with the vehicle simulator CARLA. We compare ORE with another encryption method like Homomorphic Encryption (HE) and some other privacy-preserving schemas. Besides, we quantify privacy gain and data loss utility after the data sanitization process.

TinyEnc: Enabling Compressed and Encrypted Big Data Stores With Rich Query Support

Encryption and compression are two critical techniques to ensure data confidentiality and efficiency for a cloud-based data storage system, respectively. However, directly combing encryption and compression incurs substantial performance degradation. We propose TinyEnc, an encrypted data storage system for cloud-based key-value store, which supports encryption and compression simultaneously with rich query support. To reconcile encryption and compression without compromising performance, we first propose a new encrypted compression data structure to enable fine-grained access to compressed and encrypted key-value data. We then propose two new transforming mechanisms, namely orthogonal data dividing and hierarchical data padding, to transform a plaintext key-value table into the encrypted compression data structure in a privacy-preserving way. Finally, we craft order-revealing encryption (ORE) and symmetric searchable encryption (SSE) to design a new encrypted search index over the encrypted compression data structure to support rich types of data queries. We implement a prototype of TinyEnc on top of Cassandra. Besides, the evaluation result shows that TinyEnc increases the throughput by up to 7 times and compression ratio by up to 1.3 times with respect to previous works.

Delegatable Order-Revealing Encryption for Reliable Cross-Database Query

Cloud service providers adopt pay-per-query pricing models to charge data owners based on the amount of data scanned by each query. In such models, the trustworthiness of the underlying billing system is as important as the privacy preservation for the data and queries. In this paper, we revisit delegatable order-revealing encryption (DORE), a range query algorithm allowing authorized users to retrieve data of specific ranges across multiple databases encrypted under different secret keys. We first investigate which factor in the authorization mechanism of DORE can lead to overprivileged users and let them allow any unauthorized user to query over the database of the victim without risking their credits, such as leaking the secret keys. Unfortunately, such unauthorized queries would incur unexpected financial damage to the victim in practical pay-per-query models. We then propose SEDORE, a secure order-revealing encryption scheme with resilience to unauthorized queries across databases. SEDORE features a novel user authorization mechanism limiting user privileges carefully. Consequently, the authorized users cannot illegally invite any unauthorized user to query unless they entirely leak their credits. We demonstrate that the performance of SEDORE is comparable to that of DORE while achieving a higher security level.

Secure skyline query processing in mobile edge computing over location-based data

Aiming at the efficiency and privacy of queries under mobile edge computing, secure skyline query processing in mobile edge computing over location-based data is developed. A secure skyline query framework in the mobile edge computing scenario is first proposed and a security model is also defined. To improve the efficiency of secure queries in the edge server, we use the Paillier homomorphic encryption and order-revealing encryption to devise a novel and unified secure index structure. It can effectively protect the query unlinkability in accordance with the semi-blind structure proposed by us. Furthermore, based on the secure index, we propose a secure skyline query protocol in mobile edge computing to realize the edge-cloud collaborative skyline query considering the privacy leakage. Finally, we analyze the complexity and security of the proposed approach. Findings from the experimental evaluation show that our proposed approach significantly increases the efficiency under the semi-honest model, in comparison to the state-of-art.

A blockchain-based privacy-preserving 5G network slicing service level agreement audit scheme

Network slicing, as a key technique of 5G, provides a way that network operators can segment multiple virtual logic on the same physical network and each customer can order specific slicing which can meet his requirement of 5G service. The service level agreement of network slicing (NS-SLA) of 5G, as a business agreement signed between the network operators and the customers, specifies the relevant requirements for the 5G services provided by the network operators. However, the authenticity of auditing results may not be guaranteed and the customer’s data may be leaked in the existing NS-SLA audit scheme. In this paper, a blockchain-based 5G network slicing NS-SLA audit model is proposed to address the above problems. The blockchain is applied as a public platform and all the dual monitored service parameters will be stored on the blockchain to ensure the authenticity of data. A trapdoor order-revealing encryption algorithm is introduced to audit strategy, which can encrypt the monitored parameters, realize the comparison over ciphertexts and prevent the privacy of data from leaking. Besides, an NS-SLA audit smart contract is designed to implement the audit task and execute corresponding punishment strategies automatically. We make experiments to exam the cost of the blockchain-based system and the results found clear support for the feasibility of the proposed model.

Order-Revealing Encryption: File-Injection Attack and Forward Security

Order-Revealing Encryption: File-Injection Attack and Forward Security

Fast and Secure Location-Based Services in Smart Cities on Outsourced Data

With the advancement of mobile internet, cloud computing, and smart sensing devices, Location-Based Services (LBSs) have become more and more indispensable in the Internet of Things (IoT) based smart cities. Especially, spatial keyword queries have been widely deployed in real-life applications in recent years. Recently, several privacy-preserving spatial keyword queries schemes were proposed to guarantee data security and query privacy on outsourced data. However, these schemes support neither dynamic update nor diverse query types, which cannot meet the requirements in practical applications. This paper proposes two Secure Dynamic Spatial Keyword Queries (SDSKQ) constructions that support expressive query types and dynamic update. Firstly, we present a basic SDSKQ construction based on hidden-vector encryption and order-revealing encryption. Specifically, we propose a secure hybrid index structure for spatio-textual data, named Encrypted Textual Signature Quadtree (ETSQ-tree). Using ETSQ-tree, the server can prune the index tree according to search queries to reduce the search space. Besides, the ETSQ-tree can be updated dynamically. To resist the file-injection attack, which aims to infer query information according to newly inserted objects, we further improve the basic SDSKQ to achieve forward security. We implement our two constructions and evaluate them using real-world datasets. The experimental results show that they are efficient and feasible in practical applications, and the comparative evaluation confirms that the performance of our constructions outperforms that of the state-of-the-art schemes.

Order-Revealing Encryption Scheme with Comparison Token for Cloud Computing

Order-preserving encryption (OPE) is a basic paradigm for the outsourced database where the order of plaintexts is kept in ciphertexts. OPE enables efficient order comparison execution while providing privacy protection. Unfortunately, almost all the previous OPE schemes either require numerous rounds of interactions or reveal more information about the encrypted database (e.g., the most significant bit). Order-revealing encryption (ORE) as a generalization is an encryption scheme where the order of plaintexts can be evaluated by running a comparison algorithm. Therefore, it is desirable to design an efficient ORE scheme which addresses above efficiency and security issues. In this paper, we propose a noninteractive ORE scheme from prefix encoding and Bloom filter techniques. The proposed scheme is an encryption scheme where a cloud service provider cannot evaluate the order of plaintexts until a comparison token is provided. The security analysis illustrates that our scheme achieves ideal security with frequency hiding. Furthermore, we illustrate a secure range query scheme through designing an encrypted tree structure named PORE tree from the above ORE scheme. The PORE tree reveals the order between different nodes and leaves encrypted data items in the same node incomparable even after query execution. Finally, the experimental evaluation shows the high efficiency of the proposed ORE scheme and range query scheme.

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

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 <i>et al.</i> (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 article, 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 <i>HybridORE</i>. Then, we propose an improved scheme named <i>EncodeORE</i> 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 <i>EncodeORE</i> is very practical.

New Order-Revealing Encryption with Shorter Ciphertexts

As data outsourcing services have been becoming common recently, developing skills to search over encrypted data has received a lot of attention. Order-revealing encryption (OREnc) enables performing a range of queries on encrypted data through a publicly computable function that outputs the ordering information of the underlying plaintexts. In 2016, Lewi et al. proposed an OREnc scheme that is more secure than the existing practical (stateless and non-interactive) schemes by constructing an ideally-secure OREnc scheme for small domains and a “domain-extension” scheme for obtaining the final OREnc scheme for large domains. They encoded a large message into small message blocks of equal size to apply them to their small-domain scheme, thus their resulting OREnc scheme reveals the index of the first differing message block. In this work, we introduce a new ideally-secure OREnc scheme for small domains with shorter ciphertexts. We also present an alternative message-block encoding technique. Combining the proposed constructions with the domain-extension scheme of Lewi et al., we can obtain a new large-domain OREnc scheme with shorter ciphertexts or with different leakage information, but longer ciphertexts.

Research on order-preserving encryption scheme based on CryptDB

With the rapid development of cloud computing technology, cloud database, as an important part of cloud computing services, has gradually become necessary for daily work of enterprises or individuals. However, entrusting data to third-party managing can lead to security issues such as data leakage, and users cannot guarantee data security. To this end, this paper is based on the CryptDB, an open source database encryption proxy system designed by MIT, in this thesis we propose an improvement scheme for the shortcomings of the original CryptDB system. Specific contents including: By studying the CryptDB system, we find that the system is lack of scalability for different databases and does not involve the management of system keys. For the inefficiency of the mutable Order-Preserving Encryption (mOPE) in CryptDB system, we propose an improved additive Order-Revealing Encryption (aORE) scheme by combining the Practical Order-Revealing Encryption (P-ORE) and mOPE. The scheme is based on pseudorandom function and double encryption. Compared with mOPE, it can improve the execution efficiency of the Order-Preserving scheme at the expense of security.

Secure search for encrypted personal health records from big data NoSQL databases in cloud

As the healthcare industry adopts the use of cloud to store personal health record (PHR), there is a need to ensure that we maintain the ability to perform efficient search on encrypted data (stored in the cloud). In this paper, we propose a secure searchable encryption scheme, which is designed to search on encrypted personal health records from a NoSQL database in semi-trusted cloud servers. The proposed scheme supports almost all query operations available in plaintext database environments, especially multi-dimensional, multi-keyword searches with range query. Specifically, in the proposed scheme, an Adelson-Velsky Landis (AVL) tree is utilized to construct the index, and an order-revealing encryption (ORE) algorithm is used to encrypt the AVL tree and realize range query. As document-based databases are probably the most popular NoSQL database, due to their flexibility, high efficiency, and ease of use, MongoDB, a document-based NoSQL database, is chosen to store the encrypted PHR data in our scheme. Experimental results show that the scheme can achieve secure and practical searchable encryption for PHRs. A comparison of the range query demonstrates that the time overhead of our ORE-based scheme is 25.5% shorter than that of the mOPE-based Arx (an encrypted database system) scheme.

Enabling Encrypted Rich Queries in Distributed Key-Value Stores

To accommodate massive digital data, distributed data stores have become the main solution for cloud services. Among others, key-value stores are widely adopted due to their superior performance. But with the rapid growth of cloud storage, there are growing concerns about data privacy. In this paper, we design and build EncKV, an encrypted and distributed key-value store with rich query support. First, EncKV partitions data records with secondary attributes into a set of encrypted key-value pairs to hide relations between data values. Second, EncKV uses the latest cryptographic techniques for searching on encrypted data, i.e., searchable symmetric encryption (SSE) and order-revealing encryption (ORE) to support secure exact-match and range-match queries, respectively. It further employs a framework for encrypted and distributed indexes supporting query processing in parallel. To address inference attacks on ORE, EncKV is equipped with an enhanced ORE scheme with reduced leakage. For practical considerations, EncKV also enables secure system scaling in a minimally intrusive way. We complete the prototype implementation and deploy it on Amazon Cloud. Experimental results confirm that EncKV preserves the efficiency and scalability of distributed key-value stores.

A comparative evaluation of order-revealing encryption schemes and secure range-query protocols

Database query evaluation over encrypted data can allow database users to maintain the privacy of their data while outsourcing data processing. Order-Preserving Encryption (OPE) and Order-Revealing Encryption (ORE) were designed to enable efficient query execution, but provide only partial privacy. More private protocols, based on Searchable Symmetric Encryption (SSE), Oblivious RAM (ORAM) or custom encrypted data structures, have also been designed. In this paper, we develop a framework to provide the first comprehensive comparison among a number of range query protocols that ensure varying levels of privacy of user data. We evaluate five ORE-based and five generic range query protocols. We analyze and compare them both theoretically and experimentally and measure their performance over database indexing and query evaluation. We report not only execution time but also I/O performance, communication amount, and usage of cryptographic primitive operations. Our comparison reveals some interesting insights concerning the relative security and performance of these approaches in database settings.

A framework for searching encrypted databases

Cloud computing is a ubiquitous paradigm responsible for a fundamental change in the way distributed computing is performed. The possibility to outsource the installation, maintenance and scalability of servers, added to competitive prices, makes this platform highly attractive to the computing industry. Despite this, privacy guarantees are still insufficient for data processed in the cloud, since the data owner has no real control over the processing hardware. This work proposes a framework for database encryption that preserves data secrecy on an untrusted environment and retains searching and updating capabilities. It employs order-revealing encryption to perform selection with time complexity in Θ(logn), and homomorphic encryption to enable computation over ciphertexts. When compared to the current state of the art, our approach provides higher security and flexibility. A proof-of-concept implementation on top of the MongoDB system is offered and applied in the implementation of some of the main predicates required by the winning solution to Netflix Grand Prize.

Multi-Client Order-Revealing Encryption

Order-revealing encryption is a useful cryptographic primitive that provides range queries on encrypted data since anyone can compare the order of plaintexts by running a public comparison algorithm. Most studies on order-revealing encryption focus only on comparing ciphertexts generated by a single client, and there is no study on comparing ciphertexts generated by multiple clients. In this paper, we propose the concept of multi-client order-revealing encryption that supports comparisons not only on ciphertexts generated by one client but also on ciphertexts generated by multiple clients. We also define a simulation-based security model for multi-client order-revealing encryption. The security model is defined with respect to the leakage function which quantifies how much information is leaked from the scheme. Next, we present two specific multi-client order-revealing encryption schemes with different leakage functions in bilinear maps and prove their security in the random oracle model. Finally, we give the implementation of the proposed schemes and suggest methods to improve the performance of ciphertext comparisons.