Query Privacy Research Articles

Quantum private query protocol based on counterfactual quantum key distribution with noiseless attack

Quantum private query protocol based on counterfactual quantum key distribution with noiseless attack

Optimum noise mechanism for differentially private queries in discrete finite sets

The differential privacy (DP) literature often centers on meeting privacy constraints by introducing noise to the query, typically using a pre-specified parametric distribution model with one or two degrees of freedom. However, this emphasis tends to neglect the crucial considerations of response accuracy and utility, especially in the context of categorical or discrete numerical database queries, where the parameters defining the noise distribution are finite and could be chosen optimally. This paper addresses this gap by introducing a novel framework for designing an optimal noise probability mass function (PMF) tailored to discrete and finite query sets. Our approach considers the modulo summation of random noise as the DP mechanism, aiming to present a tractable solution that not only satisfies privacy constraints but also minimizes query distortion. Unlike existing approaches focused solely on meeting privacy constraints, our framework seeks to optimize the noise distribution under an arbitrary (ϵ,δ)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(\\epsilon , \\delta )$$\\end{document} constraint, thereby enhancing the accuracy and utility of the response. We demonstrate that the optimal PMF can be obtained through solving a mixed-integer linear program. Additionally, closed-form solutions for the optimal PMF are provided, minimizing the probability of error for two specific cases. Numerical experiments highlight the superior performance of our proposed optimal mechanisms compared to state-of-the-art methods. This paper contributes to the DP literature by presenting a clear and systematic approach to designing noise mechanisms that not only satisfy privacy requirements but also optimize query distortion. The framework introduced here opens avenues for improved privacy-preserving database queries, offering significant enhancements in response accuracy and utility.

Privacy-Preserving Membership Queries for Federated Anomaly Detection

In this work, we propose a new privacy-preserving membership query protocol that lets a centralized entity privately query datasets held by one or more other parties to check if they contain a given element. This protocol, based on elliptic curve-based ElGamal and oblivious key-value stores, ensures that those 'data-augmenting' parties only have to send their encrypted data to the centralized entity once, making the protocol particularly efficient when the centralized entity repeatedly queries the same sets of data. We apply this protocol to detect anomalies in cross-silo federations. Data anomalies across such cross-silo federations are challenging to detect because (1) the centralized entities have little knowledge of the actual users, (2) the data-augmenting entities do not have a global view of the system, and (3) privacy concerns and regulations prevent pooling all the data. Our protocol allows for anomaly detection even in strongly separated distributed systems while protecting users' privacy. Specifically, we propose a cross-silo federated architecture in which a centralized entity (the backbone) has labeled data to train a machine learning model for detecting anomalous instances. The other entities in the federation are data-augmenting clients (the user-facing entities) who collaborate with the centralized entity to extract feature values to improve the utility of the model. These feature values are computed using our privacy-preserving membership query protocol. The model can be trained with an off-the-shelf machine learning algorithm that provides differential privacy to prevent it from memorizing instances from the training data, thereby providing output privacy. However, it is not straightforward to also efficiently provide input privacy, which ensures that none of the entities in the federation ever see the data of other entities in an unencrypted form. We demonstrate the effectiveness of our approach in the financial domain, motivated by the PETs Prize Challenge, which is a collaborative effort between the US and UK governments to combat international fraudulent transactions. We show that the private queries significantly increase the precision and recall of the otherwise centralized system and argue that this improvement translates to other use cases as well.

Decoy-state quantum-key-distribution-based quantum private query with error tolerance bound

Decoy-state quantum-key-distribution-based quantum private query with error tolerance bound

Preserving location‐query privacy in location‐based services: A review

AbstractLocation‐based services (LBS) leverage the geographical information of a device to deliver information, entertainment, and other services tailored to the user's present location. LBS not only brings leisure to people's lives but also raises concerns about users' privacy. Consequently, location privacy protection has captured the attention of researchers owing to the increased adoption of location‐based services and the potential privacy issues faced by users. Along with location privacy, query privacy is also a crucial privacy concern that incurs possible damage to individual privacy and even to users' safety. Preserving location privacy only secures the user's current location but protection of query privacy guarantees the security of the user's future possible location. However, to the best of our knowledge, none of the relevant studies realized the significance of query privacy. This review paper provides an overview of LBS and its components, classifying the LBS based on: granularity, number of queries, initiator, and range. We investigated the threat model, vulnerabilities, and privacy attacks in LBS, reviewed the approaches used by the researchers to mitigate the location and query privacy threats, and evaluation metrics. We also analyzed the ability of current methods to implicitly/explicitly secure query privacy and the impact of recent technological progression on problem‐building and solution evolution. Finally, this paper concludes by identifying the open issues in the existing research and directions for future work.

Decentralised Global Service Discovery for the Internet of Things.

The Internet of Things (IoT) consists of millions of devices deployed over hundreds of thousands of different networks, providing an ever-expanding resource to improve our understanding of and interactions with the physical world. Global service discovery is key to realizing the opportunities of the IoT, spanning disparate networks and technologies to enable the sharing, discovery, and utilisation of services and data outside of the context in which they are deployed. In this paper, we present Decentralised Service Registries (DSRs), a novel trustworthy decentralised approach to global IoT service discovery and interaction, building on DSF-IoT to allow users to simply create and share public and private service registries, to register and query for relevant services, and to access both current and historical data published by the services they discover. In DSR, services are registered and discovered using signed objects that are cryptographically associated with the registry service, linked into a signature chain, and stored and queried for using a novel verifiable DHT overlay. In contrast to existing centralised and decentralised approaches, DSRs decouple registries from supporting infrastructure, provide privacy and multi-tenancy, and support the verification of registry entries and history, service information, and published data to mitigate risks of service impersonation or the alteration of data. This decentralised approach is demonstrated through the creation and use of a DSR to register and search for real-world IoT devices and their data as well as qualified using a scalable cluster-based testbench for the high-fidelity emulation of peer-to-peer applications. DSRs are evaluated against existing approaches, demonstrating the novelty and utility of DSR to address key IoT challenges and enable the sharing, discovery, and use of IoT services.

Analysis and protection to user privacy in quantum private query with non-ideal light source

Analysis and protection to user privacy in quantum private query with non-ideal light source

Probabilistic versions of Quantum Private Queries

The no-go theorem regarding unconditionally secure Quantum Bit Commitment protocols is a relevant result in quantum cryptography. Such result has been used to prove the impossibility of unconditional security for other protocols such as Quantum Oblivious Transfer or One-Sided Two Party Computation. In this paper, we formally define two non-deterministic versions of Quantum Private Queries, a protocol addressing the Symmetric-Private Information Retrieval problem. We show that the strongest variant of such scheme is formally equivalent to Quantum Bit Commitment, Quantum Oblivious Transfer and One-Sided Two-Party Computation protocols. This equivalence serves as conclusive evidence of the impracticality of achieving unconditionally secure Strong Probabilistic Quantum Private Queries.

Multi-user quantum private query using Bell states

Multi-user quantum private query using Bell states

BloomDT - An improved privacy-preserving decision tree inference scheme

Outsourcing decision tree models to cloud servers can allow model providers to distribute their models at scale without purchasing dedicated hardware for model hosting. However, model providers may be forced to disclose private model details when hosting their models in the cloud. Due to the time and monetary investments associated with model training, model providers may be reluctant to host their models in the cloud due to these privacy concerns. Furthermore, clients may be reluctant to use these outsourced models because their private queries or their results may be disclosed to the cloud servers. In this paper, we propose BloomDT, a privacy-preserving scheme for decision tree inference, which uses Bloom filters to hide the original decision tree's structure, the threshold values of each node, and the order in which features are tested while maintaining reliable classification results that are secure even if the cloud servers collude. Our scheme's security and performance are verified through rigorous testing and analysis.

Improved and formal proposal for device-independent quantum private query * *This is a substantially revised and extended version of the paper [] that recently appeared in the proceedings of Indocrypt 2022. This submitted version includes additional results in sections 1.1, 1.2, 1.4, 3.1, 3.3 and in the appendix.

In this paper, we present a novel quantum private query (QPQ) scheme that is fully device-independent. As far as we know, this is the first QPQ scheme that uses EPR pairs and offers full device independence. Our approach takes into account the self-testing of shared EPR pairs and the self-testing of projective measurement operators in a mistrustful scenario where neither the client nor the server trusts each other. To achieve full device independence, we also exploit self-testing of a specific class of POVM operators used by the client in our scheme. Additionally, we evaluate the performance of our proposal formally and derive an upper limit of the maximum cheating probabilities (when the protocol does not terminate) for both the dishonest client and the dishonest server.

The Bounded Gaussian Mechanism for Differential Privacy

The Gaussian mechanism is one differential privacy mechanism commonly used to protect numerical data. However, it may be ill-suited to some applications because it has unbounded support and thus can produce invalid numerical answers to queries, such as negative ages or human heights in the tens of meters. One can project such private values onto valid ranges of data, though such projections lead to the accumulation of private query responses at the boundaries of such ranges, thereby harming accuracy. Motivated by the need for both privacy and accuracy over bounded domains, we present a bounded Gaussian mechanism for differential privacy, which has support only on a given region. We present both univariate and multivariate versions of this mechanism and illustrate a significant reduction in variance relative to comparable existing work.

Quantum Privacy Query Protocol Based on GHZ-like States

The rapid development of telemedicine technology has brought great convenience to people’s lives. Alongside generating great convenience, protecting patients’ privacy has become increasingly important. Therefore, to solve the above problems, this paper proposes a quantum privacy query protocol based on GHZ-like states. The protocol first utilizes existing diagnostic information for classification to encode symptom attributes. Then, the correlation after measurement using GHZ-like entangled states is utilized so that the querying party can only obtain a key message with the assistance of a third party. Through the above operation, the database cannot know the address of the message queried by the querying party, and the querying party is also unable to obtain more information. The protocol not only adds decoy factor detection but also adds the process of authentication while checking for illegal eavesdropping. It makes the protocol sufficiently resistant to joint attacks, participant attacks, and external system credit attacks. This database privacy query protocol ensures that the database service is more effective and its environment is more secure.

Fully device independent quantum private query

Fully device independent quantum private query

Anonymous and linkable ring signcryption scheme for location-based services in VANETs

In Location-Based Service (LBS), the vehicle user sends query messages, such as the nearest health centre, restaurant, etc., to the service provider for efficient and accurate service. The service provider gathers data regarding the received query, takes the service charge from the vehicle user, and provides these services efficiently and accurately. The service provider may reveal the vehicle user's privacy if the service provider is malicious. Similarly, if the vehicle user is malicious, he/she may also try to opt for the service from the service provider without paying the service charges. Therefore, vehicle users' queries and the service provider's data privacy are serious concerns in LBS. Some schemes, such as k-anonymity, cryptographic algorithms, etc., have been proposed to overcome these issues. The k-anonymity technique only offers vehicle users' query privacy. Cryptographic algorithms such as homomorphic encryption, oblivious transfer, etc., provide vehicle users' query and the service provider's data privacy at high communication and computation costs but reveal the vehicle user's identity. To overcome these problems, this work proposes an Anonymous and Linkable Ring Signcryption (ALRS) scheme for LBS in VANETs. It provides the service provider's data privacy and the vehicle user's query privacy, keeping the vehicle user's identity anonymous from the service provider. In addition, it maintains linkability (the number of times the same user requests a query) without revealing the identity of the vehicle user. Implementation and detailed results show that the ALRS scheme performed better than the current scheme regarding communication and computation costs and preserves all the privacy requirements.

Decoy-state quantum private query protocol with two-way communication

Quantum private query (QPQ) is a communication protocol established under the condition of mutual distrust between communication parties, which is used to solve the symmetrically private information retrieval problem in the quantum field. However, most of the existing QPQ protocols are based on the ideal light source. In practice, the multi-photon pulse generated by the transmitter will bring great security problems. We analyze the actual security of QPQ protocol in two-way communication, and find that database security and user privacy will be seriously threatened under multi-photon pulse. So we adopt the decoy state method to solve the actual security problem of QPQ protocol in view of the user side as the light source. The results show that the decoy state method is suitable for QPQ protocol of two-way communication, and can effectively defend against multi-photon pulse attacks.

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.

Confidence Intervals for Private Query Processing

Whenever randomness is involved in query processing, confidence intervals are commonly returned to the user to indicate the statistical significance of the query answer. However, this problem has not been explicitly addressed under differential privacy, which must use randomness by definition. For some classical mechanisms whose noise distribution does not depend on the input, such as the Laplace and the Gaussian mechanism, deriving confidence intervals is easy. But the problem becomes nontrivial for queries whose global sensitivity is large or unbounded, for which these classical mechanisms cannot be applied. There are three main techniques in the literature for dealing with such queries: the exponential mechanism, the sparse vector technique, and the smooth sensitivity. In this paper, for each of the three techniques we design mechanisms to produce confidence intervals that are (1) differentially private; (2) correct, i.e., the interval contains the true query answer with the specified confidence level; and (3) have a utility guarantee matching that of the original mechanism, up to constant factors. Then we show how to apply our techniques to a variety of problems ranging from simple statistics (e.g., mean, median, maximum) to graph pattern counting and conjunctive queries.

Efficient Location-Based Skyline Queries With Secure R-Tree Over Encrypted Data

Supporting efficient and secure location-based skyline queries on encrypted data, such as private data outsourced to cloud-based systems, remains an ongoing challenge for efficiency due to significant computational costs in the ciphertext domain. To accelerate privacy-preserving skyline queries, the secure index intuitively contributes to an increase in efficiency. However, designing such a secure index is a challenge while protecting the unlinkability of queries. Meanwhile, there exist little work that can commendably assure efficiency and security. In this paper, we demonstrate SecSky, an efficient solution for supporting secure location-based skyline queries through the secure index. To support SecSky, we devise a novel unified structure, named secure R-tree (SR-tree) index, without privacy leakage (especially indirect privacy). Subsequently, we propose a novel secure location-based dominance protocol, which is utilized to calculate the dominance relationship on the SR-tree. Using this protocol as the building block, our secure location-based skyline query protocol integrates SR-tree, permutation and perturbation techniques to facilitate query processing so as to dramatically reduce the computational overhead. Meanwhile, our proposed solution avoids compromising the privacy of datasets, queries, dominance relationship and skyline results. Finally, we analyze the complexity and security of SecSky. Findings from the experimental evaluation show that our proposed scheme outperforms several other protocols by at least 3 orders of magnitude in terms of query efficiency.

PPOLQ: Privacy-Preserving Optimal Location Query With Multiple-Condition Filter in Outsourced Environments

The optimal location selection is one type of the location-based services (LBS) that aims to find the best location for a new facility from some candidate facilities given a set of existing facilities and a set of customers. Due to reliable and flexible cloud services, outsourcing such heavy-computation tasks has been a popular trend. However, since the cloud is not fully trusted, and the location data contains the sensitive information, privacy protection becomes an essential requirement for these services. Although some related works have been proposed to provide privacy protection, the privacy of data and queries, accuracy of query results, and multiple features of location data are not considered by them simultaneously. In this paper, we propose a privacy-preserving optimal location query scheme PPOLQ that supports multiple-condition filter and queries over multiple data providers in outsourced environments. Specifically, we first design a secure division protocol and a secure inner product protocol based on the Paillier algorithm and the random masking technique, respectively. After that, based on the proposed algorithms, the additive homomorphic encryption, and the secure two-party computation techniques, we develop a privacy-preserving optimal location query scheme. Finally, we analyze the security of our proposed algorithms and scheme in the semi-honest model. Meanwhile, we implement all algorithms and the proposed scheme, and our implementation is open source at Gitee. We also evaluate their performances using synthetic datasets, and extensive experiments show that our scheme is practical for the real-world applications.