Privacy-preserving Range Query Research Articles

Quantum Privacy-Preserving Range Query Protocol for Encrypted Data in IoT Environments.

With the rapid development of IoT technology, securely querying sensitive data collected by devices within a specific range has become a focal concern for users. This paper proposes a privacy-preserving range query scheme based on quantum encryption, along with circuit simulations and performance analysis. We first propose a quantum private set similarity comparison protocol and then construct a privacy-preserving range query scheme for IoT environments. By leveraging the properties of quantum homomorphic encryption, the proposed scheme enables encrypted data comparisons, effectively preventing the leakage of sensitive data. The correctness and security analysis demonstrates that the designed protocol guarantees users receive the correct query results while resisting both external and internal attacks. Moreover, the protocol requires only simple quantum states and operations, and does not require users to bear the cost of complex quantum resources, making it feasible under current technological conditions.

VPRQ: Verifiable and privacy-preserving range query over encrypted cloud data

Cloud has scalable storage and massive computing power, attracting data owners to outsource their data. However, owing to privacy concerns, data is typically encrypted prior to outsourcing, which inevitably presents challenges for querying the data effectively. Although encrypted range query is one of the most prevalent types and has been widely studied, existing schemes still have some problems. They inadvertently disclose the order relationship between the upper/lower bound of a range query and the encrypted index, leading to vulnerability inference attack. Moreover, the cloud server cannot be fully trusted, which may return incorrect and incomplete query results. To deal with these issues, we present a novel verifiable and privacy-preserving range query scheme (VPRQ). The VPRQ scheme utilizes 0/1 technique to transform range comparisons into set intersections. By doing so, it effectively hides the relationship between the upper/lower bound and the encrypted index. On this basis, we design an encrypted garbled bloom filter to securely and effectively achieve range query. This ensures that VPRQ scheme effectively resists inference attack. Additionally, point-value polynomial function technology is integrated into the VPRQ protocol to provide lightweight verification for the query results. Comprehensive security analysis and proof demonstrate its effectiveness in achieving the intended design objectives. Performance evaluations illustrate the feasibility and scalability of the proposed scheme, highlighting its practical applicability.

A Novel Privacy-Preserving Range Query Scheme with Permissioned Blockchain for Smart Grid

A Novel Privacy-Preserving Range Query Scheme with Permissioned Blockchain for Smart Grid

Privacy-Preserving Range Query Quantum Scheme With Single Photons in Edge-Based Internet of Things

Range query in cloud-based outsourcing applications is an important data search service, but it can suffer from privacy disclosure. In this paper, we generalize and redefine privacy-preserving range query in edge-based Internet of Things (IoT). Furthermore, to enhance security and privacy of sensitive data, we introduce quantum cryptographic technologies and present a novel quantum approach to solve the problem. First, we present a feasible protocol for Quantum Secret Permutating (QSP) with single photons, which will be later utilized in the final scheme as a basic block. Second, we generalize traditional privacy-preserving range query to a secure one-sided two-party computation, i.e., specific dot product. Third, we present a synchronously grouping strategy based on Quantum Key Distribution (QKD) to reduce the complexity and improve the efficiency of solving this two-party computation, so that it can easily get the desired result by repeatedly using feasible quantum privacy query group by group. Finally, adopting our previously proposed quantum protocols, we design privacy-preserving range query quantum scheme in edge-based IoT. Compared with the classical related schemes, our proposed quantum scheme has higher security, because the security of our proposed protocols is based on the basic physical principles of quantum mechanics, instead of unproven computational difficulty assumptions.

Towards Efficient and Privacy-Preserving High-Dimensional Range Query in Cloud

The Internet of Things (IoT) boom has enabled Internet Service Providers (ISPs) to collect an enormous amount of high-dimensional data. Performing range queries on such data can effectively reuse them to help ISPs offer better services. Owing to the low cost and high resource utilization of cloud computing, an increasing number of ISPs are inclined to outsource data and services to it. However, as the cloud is not fully trusted, data need to be encrypted before being outsourced, which inevitably hinders many query services, e.g., range queries. Various schemes were proposed for privacy-preserving range queries, yet they struggled to extend to high-dimensional scenarios and did not support dimension selection. Aiming at this challenge, in this paper, we propose an efficient and privacy-preserving high-dimensional range query scheme (PHRQ) based on an iMinMax tree while supporting dimension selection. Specifically, we first build an iMinMax tree for high-dimensional data and utilize a symmetric homomorphic encryption technique to design a suite of privacy-preserving protocols to achieve secure high-dimensional range queries. Then, we design a sub-dimensional range determination protocol to support dimension selection. Further, based on the iMinMax tree and our privacy-preserving protocols, we propose our PHRQ scheme. Finally, security analysis shows that our scheme is privacy-preserving, and performance evaluation demonstrates that our scheme is efficient in high-dimensional range query processing.

Nereus: Anonymous and Secure Ride-Hailing Service Based on Private Smart Contracts

Security and privacy issues have become a major hindrance to the broad adoption of Ride-Hailing Services (RHSs). In this paper, we introduce a new collusion attack initiated by the Ride-Hailing Service Provider (RHSP) and a driver that could easily link the real riders and their anonymous requests (credentials). Besides this attack, existing work requires heavy computations to execute user matching, and it is challenging for riders to verify matching results. Meanwhile, a malicious driver may cancel an assigned ride order due to its short distance. To address these issues, we present a RHS system named Nereus to support collusion resistance, efficiency, verifiability, and accountability. First, we integrate a smart contract into a Software Guard Extensions (SGX) enclave to establish a <i>private smart contract</i> for collusion resistance. We use a Bloom filter to achieve efficient matching. Second, we leverage privacy-preserving range query and Merkle proofs to make matching results verifiable. Meanwhile, we adopt short group signatures to provide anonymous authentication and deposit commitments to hold the runaway driver accountable. We formally state and prove the security and privacy of Nereus. We build a prototype based on Ethereum and SGX to conduct extensive performance analysis in regard to gas costs, computational costs, and communication overhead. Experimental results show that Nereus significantly improves over existing schemes in terms of computational costs.

Study and Design of Privacy-Preserving Range Query Protocol in Sensor Networks Based on the Integration Reversal 0-1 Encoding with Bloom Filter

In the current research on data query for two-tiered WSN, the privacy-preservation range query is one of the hotspots. However, there are some problems in the existing researches in two-tiered wireless sensor networks such as high computational and communication costs for security comparison items and high energy consumption of sensing nodes. In this paper, a privacy-preservation range query protocol based on the integration reversal 0-1 encoding with Bloom filter is researched and designed. In the sensing data submission stage, the optimized reversal 0-1 encoding, HMAC algorithm, AES encryption algorithm and variable-length Bloom filter are used for generating the maximum–minimum comparison encoding and constructing a shorter verification index chain to reduce computational and communication costs of sensing nodes; in the private data range query stage, the base station uses the HMAC algorithm to convert the plaintext query range into the ciphertext query range and sends it to the storage node. In the storage node, the bitmap encoding information of the verification index chain is calculated with the comparison rule of the reversal 0-1 encoding and it is returned to the base station together with the verification index chain and the data ciphertext that compliance with the query rule; in the data integrity verification stage, the integrity of the query results using the verification index chain and bitmap encoding is verified at the base station. In the experimental section, the Cortex-M4 development board equipped with the Alios-Things operating system as sensing node and the Cortex-A9 development board equipped with the Linux operating system as storage node are implemented in this protocol, which is compared with the existing protocols in three aspects: the number of data collected in each cycle, the length of data and the number of data dimensions. The experimental results show that the energy consumption of this protocol is lower under the same experimental environment.

A Flexible and Efficient Privacy-Preserving Range Query Scheme for Blockchain-Enhanced IoT

With the increasing deployment of Internet of Things (IoT) devices in recent years, blockchain-enhanced IoT has become a new research hotspot. Due to its excellent openness, traceability and other performance, it has immensely promoted the development of this field. However, the traditional “edge-fog-cloud” three-tier architecture model for IoT still faces the threats of privacy disclosure, collusion attack, and low efficiency. To solve these problems, in this article, we creatively design a new consortium blockchain-enhanced IoT architecture, which can enable the permissioned users or participants to enjoy high-quality data services with the assistance of consortium blockchain by taking advantage of its high credibility. Based on this architecture, we propose a novel consortium blockchain-based privacy-preserving range query scheme in the decentralized IoT system, named CBCPRQ. This scheme combines consortium blockchain and inner product function encryption technology to realize flexible, safe, and batch range query of data service in edge computing. Analysis of security shows that our proposed scheme can not only protect the privacy but also perfectly resist the collusion query inference attack and the replay attack. Extensive performance evaluation is implemented by using Hyperledger Fabric platform and demonstrates that our scheme is efficient, scalable, and flexible.

A Privacy-Preserving Multidimensional Range Query Scheme for Edge-Supported Industrial IoT

Edge-supported Industrial Internet of Things (IIoT) has recently received significant attention since the edge computing can greatly improve the service quality of IIoT applications. However, edge servers are not fully trusted and are often deployed at the edge of the network. Therefore, there are some security challenges that need to be addressed. For edge-supported IIoT, a privacy-preserving range query is one of the most important functional requirements. Recently, some privacy-preserving range query solutions have been proposed in different fields. However, most of them only support single-dimensional range query, which are inefficient for the requirement of multidimensional range query. To address these problems, we propose a privacy-preserving multidimensional range query scheme for edge-supported IIoT, called Edge-PPMRQ, in this article. In Edge-PPMRQ, a novel range division algorithm is designed, through which the multidimensional ranges can be merged into one range, so as to achieve multidimensional range query through one query request. In addition, Edge-PPMRQ also supports the range queries for continuous, discontinuous, and arbitrary boundary ranges. The detailed security analysis proves that Edge-PPMRQ is privacy preserving for the query ranges, the query results, and the sensed data of IIoT devices. Furthermore, extensive comparison experiments also illustrate that Edge-PPMRQ is efficient in communication and computation.

Privacy-Aware Fuzzy Range Query Processing Over Distributed Edge Devices

Range query processing is a common edge computing and service in the Internet of things, which can extract user-interest information from distributed edge devices. How to design lightweight privacy-preserving range query processing methods remains a challenging task. Existing secure range query approaches suffer from both high communication cost and long response time, which makes them unsuitable for edge computing over resource-constrained edge devices. In this article, we propose two privacy-aware fuzzy query processing schemes based on fuzzy theory. Linguistic range variables, fuzzy overlap information, and its recovery mechanism are introduced. In addition, two distributed privacy-aware fuzzy range query processing algorithms are devised. Our approaches not only serve for privacy protection, but also aim to provide other optimal performances in terms of reliability, energy efficiency, and real-time response. Theoretical analysis and experimental evaluations based on real-world datasets validated our motivation.

PPRQ: Privacy-Preserving MAX/MIN Range Queries in IoT Networks

Range queries are widely used in several Internet-of-Things (IoT) applications as a general strategy to improve the efficiency of the system. However, the communication patterns generated by the IoT nodes could lead to the identification of the devices satisfying the query, as well as to the disclosure of the queried data. State-of-the-art solutions to address the cited security issues rely on dedicated edge/fog nodes, whose deployment could be too expensive or challenging, especially in unattended scenarios where the installation of ad hoc locations could be difficult and mains-supply is hardly available. In this article, we propose PPRQ, a resilient, scalable, and lightweight protocol that allows privacy-preserving range queries in IoT networks. PPRQ is a probabilistic scheme that can be easily adapted to MIN, MAX, and MAX/MIN range queries, while requiring only hashing and bitwise xor operations. We show that PPRQ is robust, as it can be configured to provide over 99.9% accuracy in the query results. We also prove its resiliency against passive and active adversaries for a number of interesting and realistic scenarios. Our results are rooted in sound probability theory and supported by an extensive simulation campaign, while comparisons against state-of-the-art solutions show the flexibility and adaptability of PPRQ, especially for remote and unattended scenarios. Finally, further research directions opened up by the proposed solution are also highlighted.

Fog-based Secure Service Discovery for Internet of Multimedia Things

The Internet of Multimedia Things (IoMT) has become the backbone of innumerable multimedia applications in various fields. The wide application of IoMT not only makes our life convenient but also brings challenges to service discovery. Service discovery aims to leverage location information and trust evidence scattered in a variety of multimedia applications to find trusted IoMT devices that can provide specific service in target areas. However, the eavesdropping and tampering to these sensitive IoMT data during the trust propagation process invalidate the service discovery process. To address these challenges, we propose Secure Service Discovery (SSD) for IoMT using cross-blockchain-enabled fog computing. To resist the tampering and eavesdropping during the trust propagation process, a scalable cross-blockchain structure consisting of multiple parallel blockchains is first proposed based on fog, in which different parallel blockchains can be orchestrated to propagate encrypted location information and trust evidence of different applications. Moreover, to enable a cross-blockchain structure to leverage encrypted location information and trust evidence to find trusted IoMT devices in preset areas, a novel privacy-preserving range query is proposed to query and aggregate trust evidence. Security analysis and simulations are carried out to demonstrate the effectiveness and security of the proposed SSD.

Using Reduced Paths to Achieve Efficient Privacy-Preserving Range Query in Fog-Based IoT

The fog computing architectural model has recently seen advances with respect to bandwidth and latency issues. However, since fog devices are deployed at the network edge and are not fully trustable, there are still security and privacy challenges. In this article, aiming at improving both communication efficiency and privacy protection, we propose a new efficient and privacy-preserving range query scheme in fog-based Internet of Things (IoT). We, first, introduce a new decomposition technique to efficiently interpret a given range query $[L, U]$ , where $0\leq L\leq U\leq n-1$ , as a form of inverted reduced path strings. Then, the symmetric homomorphic encryption (SHE) scheme is employed to encrypt the reduced paths and hand them over securely through a fog node to the IoT devices. This technique enables a query user to launch a privacy-preserving continuous or noncontinuous range query and receive a homomorphically aggregated encrypted response with an improved $O(\log ^{2}n)$ communication efficiency. The detailed security analysis shows that our proposed scheme is privacy preserving. In addition, extensive performance evaluations are also conducted, and the results demonstrate that our proposed scheme is by far more efficient than those previously reported schemes in terms of computational overhead and communication complexity.

Achieving O(log³n) Communication-Efficient Privacy-Preserving Range Query in Fog-Based IoT

The advance of Internet-of-Things (IoT) techniques has promoted an increasing number of organizations to explore more mission-critical solutions. However, the response latency, bandwidth usage, and reliability are still challenging issues in the traditional IoT. To tackle these challenges, the fog-based IoT has become popular and the range query is one of the most frequently used operations in fog-based IoT, where given a range query, a fog node will return the aggregated data from IoT devices to the query user. Because the fog nodes are not fully trusted, there is a desire to design a privacy-preserving range query scheme in the fog-based IoT. However, most of existing privacy-preserving range query schemes are not efficient in terms of communication overhead, especially for a large-size range. Therefore, it is still a challenging issue to design a communication-efficient range query in fog-based IoT. Aiming at this challenge, in this article, we propose a new privacy-preserving range query scheme in the fog-based IoT. Specifically, we first devise an efficient homomorphic encryption scheme for maintaining data privacy and security in a range query. Then, we present a novel range decomposition technique to compile the range query, which can transform a given range query $[L,U]$ , where $0\leq L \leq U \leq n-1$ , into a semi-triangular structure, and enable our proposed scheme to achieve $O(\log ^{3}n)$ communication efficiency. The detailed security analysis shows that our proposed scheme is really privacy preserving, and the extensive performance evaluation demonstrates that our proposed scheme is efficient in terms of low communication overhead and the computational cost.

Efficient location privacy-preserving range query scheme for vehicle sensing systems

Vehicle sensing systems (VSS) has received widespread concern in recent years. However, implementing data query efficiently while protecting data privacy in VSS is a challenge. Our work introduces an efficient location privacy-preserving range query (ELPRQ) scheme for secure VSS communications. Specifically, ELPRQ combines privacy-preserving scalar product protocol to blur the location information and sensed data, so that no one but data requester and data uploading vehicle know their own sensitive information. Detailed security analysis indicates that ELPRQ can resist all kinds of security threats and protect location privacy. Meanwhile, the performance evaluations demonstrate that ELPRQ is more efficient than the reported scheme with respect to computation complexity and communication overhead.

Privacy-preserving range query over multi-source electronic health records in public clouds

Range query is an important data search technique in cloud-based electronic healthcare (eHealth) systems. It enables authorized doctors to retrieve target electronic health records (EHRs) that are generated and outsourced by patients from the cloud server. In reality, patients always encrypt their EHRs before outsourcing, making the range query impossible. In this paper, we identify three threats in real cloud-based eHealth systems, i.e., privacy leakage, frequency analysis, and identical data inference. To capture the security properties that resist these threats, we define a security notion of indistinguishability under multi-source ordered chosen plaintext attack (IND-MSOCPA). Then, we propose a multi-source order-preserving encryption (MSOPE) scheme for cloud-based eHealth systems to enable range queries over encrypted EHRs from multiple patients. Security analysis proves that the MSOPE scheme is IND-MSOCPA secure. We also conduct comprehensive performance evaluations, which demonstrate the high efficiency of the MSOPE scheme.

A New Communication-Efficient Privacy-Preserving Range Query Scheme in Fog-Enhanced IoT

Fog-enhanced Internet of Things (IoT) has received considerable attention in recent years, as fog devices deployed at the network edge can not only improve the performance of IoT applications but also enhance the security and privacy of IoT. In this paper, we present a new communication-efficient privacy-preserving range query scheme in Fog-enhanced IoT. With the proposed scheme, both the query range and individual IoT device's data can be privacy-preserved by using BGN homomorphic encryption technique. In addition, the proposed scheme employs a range query expression, decomposition, and composition technique to reorganize the range query, which can achieve O(√n) communication efficiency. Detailed security analysis shows that the proposed scheme is really a privacy-preserving range query scheme. Extensive experiments are conducted, and the results indicate that the proposed scheme is also efficient in terms of low range query generation cost and low communication overhead.

A Secure and Efficient Privacy-Preserving Range Query Scheme in Location-Based Services

With the rapid development of wireless communications and mobile devices with location capability, location-based services (LBS) have been extensively used in almost all social and business domains. In terms of privacy and efficiency, there are two main limitations in the existing work that have to be addressed. First, they fail to preserve the location and content privacy simultaneously. Second, they do not support the range queries. In this paper, we propose a secure and efficient privacy-preserving range query protocol in the LBS, which satisfies the three requirements of the location privacy and content privacy protection, query efficiency, and scalable index size. To achieve location privacy and content privacy, we adopt the prefix membership verification scheme to encode index elements and a bloom filter to store index elements. To achieve query efficiency and scalable index size, a balanced dynamic binary tree structure called $DBtree$ is proposed. Furthermore, to improve the query efficiency, we propose both the index element encoding optimization and traversal optimization algorithms. Finally, we evaluate our scheme both on the simulated and real-world datasets. The experimental results demonstrate the security and efficiency of our scheme.

Achieve Location Privacy-Preserving Range Query in Vehicular Sensing.

Modern vehicles are equipped with a plethora of on-board sensors and large on-board storage, which enables them to gather and store various local-relevant data. However, the wide application of vehicular sensing has its own challenges, among which location-privacy preservation and data query accuracy are two critical problems. In this paper, we propose a novel range query scheme, which helps the data requester to accurately retrieve the sensed data from the distributive on-board storage in vehicular ad hoc networks (VANETs) with location privacy preservation. The proposed scheme exploits structured scalars to denote the locations of data requesters and vehicles, and achieves the privacy-preserving location matching with the homomorphic Paillier cryptosystem technique. Detailed security analysis shows that the proposed range query scheme can successfully preserve the location privacy of the involved data requesters and vehicles, and protect the confidentiality of the sensed data. In addition, performance evaluations are conducted to show the efficiency of the proposed scheme, in terms of computation delay and communication overhead. Specifically, the computation delay and communication overhead are not dependent on the length of the scalar, and they are only proportional to the number of vehicles.

VP2RQ: Efficient verifiable privacy-preserving range query processing in two-tiered wireless sensor networks

In the field of wireless sensor networks, the secure range query technique is a challenging issue. In two-tiered wireless sensor networks, a verifiable privacy-preserving range query processing method is proposed that is based on bucket partition, information identity authentication, and check-code fusion. During the data collection process, each sensor node puts its collected data into buckets according to the bucket partition strategy, encrypts the non-empty buckets, generates the check-codes for the empty buckets, and fuses them. Then, the check-codes and the encrypted buckets are submitted to the parent node until they reach the storage node. During query processing, the base station converts the queried range into the interested bucket tag set and sends it to the storage node. The storage node determines the candidate-encrypted buckets, generates the check-code through code fusion, and sends them to the base station. The base station obtains query results and verifies the completeness of the result with the check-code. Both the theoretical analysis and experimental results show that verifiable privacy-preserving range query is capable of protecting the privacy sensor data, query result, and query range, which also supports the completeness verification of the query result. Compared to existing methods, verifiable privacy-preserving range query performs better on communication cost.