Federated Learning (FL) has become a promising method for training machine learning models while protecting patient privacy. This systematic review examines the use of privacy-preserving techniques in FL within decentralized healthcare systems. It compares existing methods such as Differential Privacy (DP), Trusted Execution Environment (TEE), Zero Knowledge Proofs (ZKP), Homomorphic Encryption (HE), Watermarking, Blockchain, and Secure Multi-Party Computation (SMPC) based on regulatory compliance, scalability, computational cost, complexity, and mathematical foundations. The principle challenges in decentralized healthcare like heterogeneous data, privacy risks, security threats, and compliance issues have been discussed. The review also highlights the importance of adhering to global regulations like HIPAA, GDPR, and country-specific data protection laws. Furthermore, it discusses open challenges and suggests future research directions to overcome current limitations, including computational efficiency, adversarial attacks, and the creation of policy frameworks for standardization. Overall, this review provides a unique perspective on ethical, secure, and scalable privacy-preserving FL models for the next generation of healthcare applications. ⢠Analyzes essential techniques: Differential Privacy, SMPC, HE, TEE, ZKP, and Blockchain. ⢠Reviews key privacy techniques: DP, SMPC, HE, TEE, ZKP, and Blockchain. ⢠Compares methods based on cost, scalability, and resilience in FL. ⢠Identifies issues such as non-IID data, high communication, and compliance. ⢠Suggests hybrid and hardware-aided frameworks for secure FL. ⢠presents future needs in terms of explainability, interoperability, and quantum security.
Modern cyber threats, known for their complexity and constant change, surpass traditional intrusion detection systems (IDS). This paper explores a new security approach that combines Artificial Intelligence (AI) with decentralized architectures to develop IDS that are robust, scalable, and protect user privacy. It examines the core roles of Federated Learning (FL) and Blockchain, highlighting three main research challenges: The vulnerability of AI models to adversarial attacks, privacy and data integrity concerns in collaborative learning, and performance limitations in distributed systems. To address these issues, we suggest solutions such as adversarial training, differential privacy, and lightweight consensus mechanisms. Our analysis of case studies shows that hybrid FL-Blockchain systems outperform traditional methods in practical application environments.
This paper presents a decentralized, trustless architecture for IoT data trading, leveraging a subscription-based economic model, decentralized publish/subscribe communication, and Distributed Ledger Technologies (DLTs). The system addresses scalability, integrity, confidentiality, and privacy challenges while ensuring financial incentives through end-to-end encrypted transmission and blockchain-based smart contracts. By integrating established standards and decentralized identity management, the framework secures digital rights, clarifies data ownership, and enables flexible trading mechanisms. The proposed architecture supports trustless information exchange, guarantees secrecy in IoT-based sensor data sharing, and fosters economic incentives for stakeholders. Evaluations focus on system performance, efficiency in encrypted data transmission, and trading cost optimizations. Future work includes optimizing Gas usage, enhancing broker operations, implementing a decentralized search engine for data products, and refining qualifications for data providers and brokers. This research contributes to the development of secure, scalable, and privacy-preserving IoT data trading solutions, ensuring efficient and transparent transactions.
Keqiu Li, Changzhi Li, Yang Shi, Dengcheng Hu ¡ 7 authors
Blockchain-based Federated Learning (BCFL) has attracted considerable attention in the intelligent IoT domain for its privacy-preserving and decentralized characteristics. Depending on their applicable scenarios, BCFL frameworks are categorized into two types: synchronous and asynchronous. However, synchronous BCFL struggles with low efficiency in heterogeneous IoT environments, while asynchronous BCFL suffers from slow convergence speed. In additional, Both BCFL incur significant resource consumption from blockchain consensus mechanisms which is unrelated to federated learning tasks, leading to resource wastage and poor scalability, making them unsuitable for large-scale IoT networks. To address these challenges, we propose CoCFL, a novel BCFL framework utilizing multi-chain collaboration. CoCFL introduces two lightweight sub-chains: PoCFL-CChain and PC-CChain, based on different FL strategy. PoCFL-CChain uses a synchronous FL strategy for learning devices with similar performance to generate high-accuracy models, while PC-CChain adopts an asynchronous strategy for heterogeneous devices, which can improving training efficiency. CoCFL assigns devices to suitable sub-chains based on their performance to carry out FL tasks and aggregates the sub-chain models into a global model. This multi-chain collaboration strategy enhances model accuracy and convergence speed and significantly improves the scalability of BCFL. In additional, the consensus mechanisms in CoCFL sub-chains not only maintain the blockchain ledger but also handle FL-related tasks such as detecting poisoning attacks, assigning roles, and distributing incentives. This design not only improving the efficiency of BCFL, but also enhances learning security and ensuring fair incentives. Experiments show that CoCFL improves learning accuracy by 6% and efficiency by 18% over existing BCFL frameworks. It also demonstrates excellent scalability, with time consumption liner decreasing as sub-chains increase, and can withstand up to 40% of poisoning attacks while ensuring fair incentives.
In our Society public opinion surveys are necessary for understanding societal viewpoints, but the conventional polling platforms are at a high risk of forgery, data breaches and manipulation. To ensure the reliability and integrity of polling outcomes, there is a growing need for secure and transparent mechanisms [1]. This paper presents ElectraGuard, a blockchain-based online polling platform designed to deliver trustworthy, tamper-resistant, and user-friendly opinion polling. Built on the Ethereum blockchain using smart contracts, ElectraGuard ensures decentralised execution, voter anonymity, and one-response-per-participant integrity [2]. The system leverages cryptographic hashing and distributed ledger technology to record each submission immutably, preventing result falsification or post-hoc modification. Supporting multiple categories of institutional and organizational polls, the platform features a web-based interface that enables secure participation and real-time result visualization. Testing shows that ElectraGuard greatly improves the security, ability to check results, and trustworthiness of online polling, providing a strong base for clear and checkable digital surveys.
Ahmed Anwer Jaafa, Madhu Sahu, M. Jasmin, Mamadjanova Zukhra Bakhromjanovna ¡ 8 authors
Sharing patient data safely and efficiently is still hard in the constantly changing world of digital healthcare because of worries about privacy, giving consent, and how different systems work together. This paper suggests using ChainMedX, which relies on blockchain technology to let patients, doctors, and other healthcare professionals exchange data in real time with dynamic consent consent management. ChainMedX uses permissions, smart contracts, and zero-knowledge proofs to let patients pick who can have access to their medical records, manage exactly what is shared, and specify the time period the sharing is needed, making sure those permissions cannot be altered. Being distributed across both cloud and edge servers, the patient-managed encrypted data vaults make active updates of medical records possible, complying with FHIR standards. Thanks to an AI-based consent suggestion module, patients receive useful advice that suits their needs and the current emergency situation. In addition, ChainMedX deals with urgent issues, such as fast access with easy âbreak-glassâ rules and complete tracking of every transaction, and makes it easier for healthcare services to interact with the wider health organization and verify insurance policies. According to the results, latency, security, and managing consent are all better in the new system than in systems that operate centrally or rely on blockchain. The research mentions that connecting blockchain, edge computing, and privacy-based cryptography can form a healthcare system that respects patient data privacy and makes healthcare cooperation speedy and secure. The goal of this framework is to help provide for data exchange between countries, while including new forms of digital health technology, all this aims to strengthen patient trust and the integrity of their medical data, along with providing better healthcare outcomes.
Revocable ring signatures protect signer anonymity. A trusted authority can revoke signing rights when necessary. This makes them attractive for blockchains and vehicular networks. Existing lattice-based ring signature schemes are only traceable. They can de-anonymize a malicious signer, yet fail to stop the revoked signer from creating a valid signature. This contradicts the very notion of revocation. We introduce a polynomial-based revocation list. It is enforced with non-interactive zero-knowledge proofs of knowledge. Our protocol implicitly verifies the up-to-date revocation list during signature generation. Consequently, revoked signers cannot authenticate and are effectively excluded. Integrating this mechanism into a lattice setting, we obtain a compact revocable ring signature. The scheme is correct, anonymous, unforgeable, and truly revocable under the random oracle model. No costly key updates are required. Compared with prior trace-and-update schemes, our construction offers a practical post-quantum primitive. It guarantees both privacy and accountable revocation. Overhead analysis shows that we have added very little cost while ensuring true revocation.
K. Swathi, Putta Durga, K. Venkata Prasad, A Krishna Chaitanya ¡ 7 authors
An enormous demand for a secure, scalable, intelligent edge computing framework has emerged for the exponentially increasing number of Internet of Things (IoT) devices for any substrate of modern digital infrastructure. These edge nodes distributed across heterogeneous environments serve as primary interfaces for sensing, computation, and actuations. Their physical deployment in unattended scenarios puts them at risk of being targets for resource manipulation. One widely accepted IoT architecture with traditional notions of edge may consider a threat to its centralized knowledge with an unbounded attack surface that includes anything that can remotely connect to the edge from the cloud-like domain. Existing strategies either forget the dynamic risk context of edge nodes or do not achieve a reasonable trade-off between security and resource constraints, essentially degrading the robustness and trustworthiness of solutions intended for real-life scenarios. To address the existing gaps, the work presents a novel Blockchain Integrated Deep Learning Framework for secure IoT edge computing, introducing a hybrid architecture where the transparency of blockchain meets deep learning flexibility. The proposed system incorporates five specialized components: Blockchain-Orchestrated Federated Curriculum Learning (BOFCL), which ensures risk-prioritized training using threat indices derived from blockchain logs; this adaptive sequencing enhances responsiveness to high-risk edge scenarios. Zero-Knowledge Proof Enabled Secure Inference Engine (ZK-SIE) provides verifiable privacy-preserving inference, ensuring model integrity without exposing input data or model internals in process. Blockchain Indexed Adversarial Attack Simulator (BI-AAS) focuses on testing the models in edge environments against attack scenarios drawn from common adversarial profiles and thereby facilitates a model defensive retraining. Energy-Aware Lightweight Consensus with Adaptive Synchronization (ELCAS) avoids overhead by seeking energy-efficient participants for global model synchronization in constrained environments. Trust Indexed Model Provenance and Deployment Ledger (TIMPDL) ensures model lineage tracking and deploy ability in a transparent manner by providing composite trust scores computed from data quality, node reputation, and validation metrics. Altogether, the framework combines the data integrity, adversarial robustness, and trust-aware deployment, shortening training latency, synchronization energy, and privacy leakage. It is a foundational advancement supporting secure decentralized edge intelligence for next-generation IoT ecosystems.
Modern cryptographic tools such as multi-party computation (MPC) and zero-knowledge proofs (ZKPs) offer strong, provable security guaranteesâbut these generic protocols remain impractical for production-scale machine learning (ML), especially in the era of large language models (LLMs). This thesis proposal advances the central claim that cryptographic protocols co-designed with the structure of specific ML subtasks can achieve practical efficiency without compromising privacy or verifiability. To validate this vision, this proposal develops three interconnected research thrusts: (1) Confidential Outsourced Training. Customized MPC protocols shift expensive cryptographic steps to local computations, enabling secure training of large models in untrusted clouds by resource-constrained data owners. (2) Scalable MPC Primitives for Large Datasets. Provably secure building blocksâsuch as oblivious shuffles, private joins, and sparse linear algebra routinesâbridge the performance gap in privacy-preserving data pipelines at scale. (3) Verifiable ML without Retraining. Rather than proving each training step, a new proof-of-optimality framework certifies that a trained or fine-tuned model (e.g., LoRA adapters) satisfies desired properties, enabling efficient, auditable deployment without re-executing training. Together, these efforts aim to close the long-standing gap between privacy and efficiency, demonstrating that strong cryptographic guarantees and modern ML workflows can be reconciled through principled, application-aware design.
We introduce the notion of committed vector oblivious linear evaluation (C-VOLE), which allows a party holding a pre-committed vector to generate VOLE correlations with multiple parties on the committed value. It is a unifying tool that can be found useful in zero-knowledge proofs (ZKPs) of committed values, actively secure multi-party computation, private set intersection (PSI), etc.
M. Saravana Karthikeyan, R. Rajasree, R. Santhana Krishnan, C. Gayathri ¡ 6 authors
Secure and efficient healthcare data sharing is critical for modern medical ecosystems, yet existing systems often suffer from limited scalability, privacy risks, and lack of intelligent data management. This study proposes MedVault, a hybrid blockchain-cloud-AI framework designed for secure, patient-centric healthcare data management. The architecture employs Corda for on-chain storage of consent records, metadata, and audit logs, while large medical datasets are encrypted and stored off-chain in AWS S3 with PostgreSQL metadata management. Security is reinforced using AES-256 encryption, Proxy Re-Encryption (PRE), Zero-Knowledge Proofs (ZKP), and decentralized identity management via Hyperledger Indy and Aries, while a FHIR-based gateway ensures seamless integration with electronic health records (EHRs). Intelligence is incorporated through deep learning models, including Autoencoders for anomaly detection, CNNLSTM for medical data analytics, Graph Neural Networks (GNNs) for consent prediction, DNNs for risk assessment, and Federated Learning (FL) for privacy-preserving distributed model training. Variational Autoencoders (VAEs) generate synthetic datasets, and Explainable AI techniques (SHAP, LIME) ensure interpretability. Extensive evaluations demonstrate that Corda-MedVault outperforms Hyperledger Fabric, Ethereum, and traditional centralized approaches across metrics such as blockchain latency, throughput, auditability, off-chain storage efficiency, energy consumption, anomaly detection, and consent prediction. Overall, the proposed system provides a scalable, energy-efficient, privacypreserving, and intelligent platform for real-time healthcare data sharing, offering a robust solution for secure and compliant medical data management.
Artificial Intelligence as a Service (AIaaS) enables users to query a model hosted by a service provider and receive inference results from a pre-trained model. Although AIaaS makes artificial intelligence more accessible, particularly for resource-limited users, it also raises verifiability and privacy concerns for the client and server, respectively. While zero-knowledge proof techniques can address these concerns simultaneously, they incur high proving costs due to the non-linear operations involved in AI inference and suffer from precision loss because they rely on fixed-point representations to model real numbers.
In many fields, the need to securely collect and aggregate data from distributed systems is growing. However, designs that rely solely on encrypted data transmission make it difficult to trace malicious users. To address this challenge, we have enhanced the secure aggregation (SA) protocol proposed by Bell et al. (CCS 2020) by introducing verification features that ensure compliance with user inputs and encryption processes while preserving data privacy. We present LZKSA, a quantum-safe secure aggregation system with input verification. LZKSA employs seven zero-knowledge proof (ZKP) protocols based on the Ring Learning with Errors problem, specifically designed for secure aggregation. These protocols verify whether users have correctly used SA keys and their Lâ, L2 norms and cosine similarity of data, meet specified constraints, to exclude malicious users from current and future aggregation processes. The specialized ZKPs we propose significantly enhance proof efficiency. In practical federated learning scenarios, our experimental evaluations demonstrate that the proof generation time for Lâ and L2 constraints is reduced to about 10-3 of that required by the current state-of-the-art method, RoFL (S&P 2023), and ACORN (USENIX 2023). For example, the proof generation/verification time of RoFL, ACORN and LZKSA for Lâ is 94s/29.9s, 78.7s/33.9s, and 0.02s/0.0062s for CIFAR10, respectively.
Daria Schumm, Gabriel Stegmaier, Cedric von Rauscher, Katharina Mßller ¡ 5 authors
Blockchains raise new privacy challenges, especially in Decentralized Identity (DI) and Self-Sovereign Identity (SSI) systems. Zero Knowledge Proofs (ZKPs) offer privacy, but only allow binary verification. Homomorphic Encryption (HE) enables flexible operations on encrypted data (e.g., addition, multiplication) but lacks comparison support. This paper addresses this gap by introducing a privacy-preserving comparison operation within HE, presenting the first comprehensive comparison of ZKP and HE as privacy-preserving mechanisms.
Abstract Local Energy Communities (LECs) are gaining prominence as key actors in the transition toward sustainable and decentralized energy systems. A critical challenge for these communities lies in achieving energy self-sufficiency through effective forecasting of energy production and consumption. Accurate forecasting models are essential to support optimization and planning strategies. However, privacy concerns and regulatory constraints often limit the feasibility of centralized data-driven approaches, as users are understandably reluctant to share their consumption data. To address this issue, we propose a privacy-preserving forecasting framework based on Federated Learning (FL) and Long Short-Term Memory (LSTM) networks, which enables collaborative model training without disclosing raw user data. Building upon this core architecture, we further enhance transparency and user engagement by introducing Zero-Knowledge Proofs (ZKPs) for secure inference verification, and a novel incentive layer based on dynamic Non-Fungible Tokens (dNFTs) and fungibile tokens. Our approach ensures model integrity, protects user data, and fosters sustainable behavior through verifiable, trustless reward mechanisms. Experimental results demonstrate the feasibility and potential of this architecture in supporting privacy-aware, decentralized energy forecasting within LECs.
Trading of data is increasingly prevalent as data gain significant economic value, but existing data exchange schemes often suffer from third-party dependency, high verification costs, or inadequate protection of fairness and confidentiality. An efficient decentralized fair exchange scheme for data trading which uses cryptographic commitment scheme and smart contract was proposed in this paper. Our solution guarantees exchange fairness, which requires payments and data to be exchanged correctly between the data buyer and the data seller. First, we design a data verification method with constant verification cost by using polynomial commitments, ensuring that the buyer receives the data matching an agreed-upon commitment. Second, we employ smart contracts to complete the atomic exchange of data and funds, and design a key transmission method by using the properties of bilinear pairings to ensure the confidentiality of trading data. Moreover, our scheme was proved to satisfy the desired security properties: seller fairness, buyer fairness and confidentiality. Simulation results demonstrate the efficiency and practicality of the proposed scheme.
Incident reporting systems are integral to maintaining accountability and transparency across critical domains such as cybersecurity, healthcare, and public governance. However, existing centralized mechanisms are prone to manipulation, data loss, and unauthorized modifications. This paper proposes 'IntegriChain', an intelligent and decentralized incident reporting framework that combines Blockchain technology and Artificial Intelligence (AI). The system ensures tamper-proof data storage through SHA-256 hashing and distributed ledger technology while leveraging AI for incident classification, anomaly detection, and risk prediction. This hybrid approach improves security, reliability, and efficiency in reporting workflows. The framework is designed to serve as a scalable solution applicable to multi-domain reporting systems where trust, immutability, and intelligent analysis are critical.
Energy saving is need of hour and effective energy management for Smart grids is no exception. Efficient management of Smart grids is complex task and thus needs state-of-the-art technologies for efficient management and ensuring data privacy. For secured and efficient management the emerging technologies like Federated Learning and Blockchains can be deployed. The integration of Federated learning and Blockchain offers a promising solution for these advanced and decentralized energy management systems. Federated Learning is distributed machine learning approach where the model is trained over multiple decentralized devices. data centralization. Blockchain technology offers, immutable,secured distributed ledger system that complement the Federated Learning framework. The integration of Federated learning and Blockchain facilitates secure tamper proof data analytics, which can transform energy management systems. The present research paper proposes a model for Smart grids which is based on integration of FL and BC technologies. The study discusses System Architecture for FL-BC framework. Study also proposes the possible simulation for real-world smart grid scenario consisting of smart energy devices like smart meters, solar panels, industrial IoT sensors, and Home Energy Management Systems (HEMS). It can be simulated using Python with the help of machine learning libraries like TensorFlow, PyTorch.
Graph learning has garnered increasing attention in recent years, which aims to train machine learning models over graph data to support various graph analytic tasks. Coming with the popularity of graph learning are critical privacy concerns regarding the information-rich graphs in many application domains (e.g., finance, social networks, and healthcare). There is thus an urgent call for privacy-preserving graph learning. In this paper, we target an emerging decentralized graph scenario, where a graph is fully decentralized among a set of nodes in such a way that each node only has a limited local view about the global graph. We propose PDGL, a new system framework that can effectively support privacy-assured model training over a decentralized graph, with privacy protection for the links among the nodes as well as the nodesâ private feature data and labels. In contrast to PDGL, prior work does not provide protection for the nodesâ links, feature data, and labels simultaneously. Extensive experiments demonstrate that while providing strong privacy protection for decentralized graph data, PDGL can achieve model utility comparable to the baseline setting of centralized graph learning.
Federated learning (FL) offers a distributed approach for the collaborative training of machine learning models across decentralized clients while safeguarding data privacy. This characteristic makes FL well suited for privacy-sensitive fields such as healthcare and finance. However, addressing the heterogeneity caused by nonindependent and identically distributed (non-IID) data remains a significant challenge for traditional FL methods. To address these issues, the enhancing clustered federated learning with adaptive similarity (AS-CFL) algorithm, which dynamically forms client clusters based on model update similarity and uses a forward-incentive mechanism to improve collaborative training efficiency among similar clients, is proposed in this study. Experimental results on the MNIST and EMNIST datasets reveal that compared with baseline methods such as the CFL, IFCA, and FedAvg models, the AS-CFL algorithm achieves faster convergenceâreducing the number of communication rounds by approximately 20%âwhile maintaining competitive accuracy, demonstrating its effectiveness in heterogeneous FL scenarios.
The rise of collaborative AI, particularly in distributed Mixture-of-Experts (MoE) systems, has created a critical challenge: how to ensure trust and transparency when aggregating proprietary models from different providers. To address this, we introduce a novel cryptographic protocol ZQ-WMA that enables verifiable and privacy-preserving online learning. Our method integrates zero-knowledge proofs with a quantized version of the Weighted Majority Algorithm, allowing a central aggregator to publicly prove it is honestly combining expert advice and updating weights according to the agreed-upon rules, all without revealing any confidential model parameters.This approach ensures that expert contributions are evaluated fairly and protects valuable intellectual property. Our analysis reveals that the quantization necessary for the zero-knowledge proofs can counter-intuitively enhance prediction accuracy, a phenomenon we attribute to the maximal entropy random walks. Furthermore, our benchmarks demonstrate the efficiency of this method, showing proof generation complexity less than 10% of a standard SHA256 hash function, with O(1) proof size and verification time. This work provides a practical and scalable framework for building trustworthy collaborative AI systems.
This paper presents a secure aggregation system Armadillo that has disruptive resistance against adversarial clients, such that any coalition of malicious clients can affect the aggregation result only by misreporting their private inputs in a pre-defined legitimate range. Armadillo is designed for federated learning setting, where a single powerful server interacts with many weak clients iteratively to train models on client's private data. While a few prior works consider disruption resistance under such setting, for an aggregation on n clients they either require high cost per client (Chowdhury et al. CCS '22) or concretely many rounds that is logarithmic in n (Bell et al. USENIX Security '23). Although disruption resistance can be achieved generically with zero-knowledge proof techniques (which we also use in this paper), we realize an efficient system with two new designs: 1) a simple two-layer secure aggregation protocol that requires only simple arithmetic computation; 2) an agreement protocol that removes the effect of malicious clients from the aggregation with low round complexity. With these techniques, Armadillo runs in 3 rounds per aggregation (our round complexity is independent of n) with computationally lightweight server and clients.
Consensus algorithms are essential for blockchain networks to achieve agreement on transaction outcomes. However, mainstream algorithms like Proof of Work (PoW) and Proof of Stake (PoS) exhibit significant limitations in security and efficiency, including high energy consumption, wealth centralization, and a lack of effective node behavior evaluation to guard against internal attacks. To address these issues, this paper proposes an intelligent reputation-based consensus mechanism leveraging a Long Short-Term Memory (LSTM) network. This mechanism analyzes multi-dimensional node attributes (e.g., hostname, country, event sequence, timestamp) to model behavioral patterns using the LSTM, enabling accurate reputation quantification and early detection of malicious intent. Furthermore, we design a dynamic reputation scoring system that calculates a composite reputation score by weighting the LSTMâs predicted score against the nodeâs historical behavior score. This composite score is directly applied to the dynamic election of authoritative nodes and their role assignment within the consensus process. Simulation results demonstrate that, compared to traditional PoW and PoS mechanisms, our approach significantly reduces the attack success rate of malicious nodes attempting to form monopolies, thereby enhancing the fairness of the consensus process and the overall robustness of the system.
With the rapid development of the Internet of Things (IoT), Location-Based Services (LBS) have been widely applied in smart transportation, mobile social networking, and urban sensing. However, the high sensitivity of precise location data makes it a primary source of privacy breaches. Existing privacy-preserving solutionsâsuch as k-anonymity, differential privacy, homomorphic encryption, or decentralized architecturesâthough partially mitigating risks, still rely on trusted third parties for anonymous set generation, key management, or query scheduling, leading to single points of failure, centralized trust, and potential misuse. Even decentralized proposals struggle to balance service quality with strong privacy guarantees, efficient verification, and lightweight deployment. To address this, this paper proposes a lightweight blockchain-based decentralized LBS privacy-preserving framework. This solution eliminates trusted intermediaries: users locally generate privacy-constrained fuzzy regions and construct zero-knowledge proofs (ZKPs) to cryptographically verify their actual locations within these regions. The proofs are submitted to blockchain smart contracts for public verification; only upon successful validation do distributed LBS nodes respond with candidate results, which are finalized through local user filtering. Theoretical analysis and experiments demonstrate that our framework effectively resists privacy inference from semi-honest service providers and external attackers, achieving a balance among query accuracy, response latency, and computational overhead. This provides a viable path for building secure, efficient, and user-centric LBS systems.