Blockchain and distributed hash table based covert mobile query method

Abstract

A kind of concealed mobile query method based on blockchain and distributed hash table, first, construct the distributed hash table DHT network BDN based on blockchain enhancement BDN;Then, in the query scene under BDN, by mobile user initiates request and is completed by the concealed cooperation of adjacent user.BDN, blockchain is used as backbone network, bears the core function of transaction processing and smart contract execution;DHT network is used to access the geographic information of user, to support user mobile query.Query-oriented user concealed cooperation and behavior verification are respectively run on side chain and main chain network;Blockchain data layer accesses the user information in DHT network by index and storage interface;Message type in the P2P communication protocol of blockchain network layer is expanded to support information transmission in DHT network;Full node in blockchain bears the function of DHT network storage node;Smart contract guarantees the security of DHT access;MPT is introduced to optimize data index and retrieval.

Description

Technical Field

[0001] This invention belongs to the field of network security technology, specifically a covert mobile query method based on blockchain and distributed hash tables. Background Technology

[0002] With the widespread adoption of smart mobile devices, mobile querying has become a crucial foundation for supporting various context-aware applications, enabling users to access information related to their environment, interests, or tasks at any time. Unlike traditional static retrieval, mobile queries are typically accompanied by high-frequency interactions, multi-party participation, and dynamic contextual changes, potentially revealing user identity characteristics, query behavior patterns, and communication relationships. [1] Even if the query content is encrypted, malicious observers can still continuously track and infer user behavior patterns through traffic pattern analysis and correlation inference. [2] .

[0003] Typical privacy protection mechanisms include introducing a centralized trusted entity, user-side perturbation, and multi-node collaborative queries. [3] In open mobile environments, the ability of a single user to independently complete a query inevitably leads to issues of behavioral identifiability, making node collaboration a fundamental form of privacy protection in mobile queries. However, without a unified foundation of trust and constraints, the collaboration process may introduce new attack surfaces and even be exploited by malicious nodes.

[0004] Covert queries further reduce the observability of query interactions at the communication level, making it difficult for external observers to distinguish between genuine queries and normal network activity. [4][5] Unlike traditional privacy protection mechanisms, these methods do not focus on perturbing the query content itself, but rather on hiding the query process and communication relationships. Covert queries are a feasible solution when query size and interaction frequency are limited. However, for multimodal queries involving rich media... [6] The challenge of covert queries has shifted from "whether they can be hidden" to "whether they can remain effective in large-scale scenarios".

[0005] Blockchain technology, due to its decentralized trust and auditable characteristics, is considered a potential infrastructure for enhancing the credibility of collaborative covert queries. [7] Research shows that, under lightweight query conditions, introducing blockchain helps reduce reliance on the honesty of participating nodes and improves the verifiability of the collaboration process. [8][9] However, blockchain is not designed for high-frequency, large-scale data interaction, and its transaction structure and operating mechanism inevitably incur additional overhead when facing high-load queries.

[10] When queries involve multimodal data, traditional blockchain architectures often struggle to simultaneously support trusted collaboration and efficient data interaction.

[0006] In untrusted environments, ensuring the trustworthiness of collaboration while achieving efficient query interaction is key to enabling covert queries through blockchain, and this manifests in the following aspects in multimodal query scenarios:

[0007] 1) Distributed data access supporting large-scale queries. The redundant storage and verification overhead of blockchain makes it difficult to support high-frequency, large-scale rich media interactions. Several solutions combining on-chain trust and off-chain storage have been proposed to compensate for the insufficient on-chain data carrying capacity. BlockStack

[11] A decentralized naming system is built using blockchain technology, and data is located using a distributed hash table (DHT). (Chen et al.)

[12] Integrating the Interplanetary File System (IPFS) with blockchain to achieve distributed file management. (Nizamuddin et al.)

[13] Smart contracts and decentralized storage are used to achieve document traceability and tamper-proofing. These solutions improve scalability by hosting large objects off-chain and retaining indexes / audit information on-chain. LVMT

[14] It is a high-performance blockchain-native storage structure that reduces storage I / O overhead through multi-layered versioned multi-prefix trees, alleviating on-chain data access bottlenecks. Measurements of decentralized storage on-chain show that accessibility and retrieval performance are affected by network and content distribution, making it difficult to support real-time access.

[15] Blockchain sharding mechanisms can improve throughput, but they also complicate load management.

[16] Although scalability and fault tolerance have been improved, the above methods are mostly focused on static or general data management and cannot adapt to high-frequency mobile queries.

[0008] 2) Real-time covert querying for multimodal data. Covert communication exacerbates the verification and storage pressure on blockchain networks, leading to network congestion and latency. Existing blockchain covert communication mainly relies on transaction embedding, which, limited by capacity and efficiency, is insufficient to meet the needs of multimodal queries. Cao et al.

[17] The proposed public-key chain-based data embedding method improves both concealment and transaction screening efficiency. Some researchers embed messages into the default storage parameters of transactions and utilize special signatures to enhance transaction identification efficiency.

[18] To enhance data carrying capacity, Tian et al.

[19] A blockchain covert communication scheme based on transaction amount encoding is proposed, but a trade-off between embedding capacity and transaction overhead needs to be struck. (Based on the literature...)

[20] Under high-frequency interaction conditions, the verification and broadcasting processes of blockchain systems become performance bottlenecks for real-time covert communication. Furthermore, TCCM...

[21] ORCA is a three-way covert communication model that combines blockchain and IPFS. It enhances data carrying and transmission capabilities through IPFS covert channels, ledger-level covert channels, and network-level covert channels.

[22] It is a covert communication framework based on strictly orthogonal coding, supporting covert group communication, receiver isolation, and resistance to speculative attacks, with high embedding capacity and statistical indistinguishability. Nevertheless, simply improving the embedding is insufficient to eliminate the latency accumulation caused by on-chain interactions and is difficult to support high-frequency, large-scale query scenarios.

[0009] 3) Trusted queries in untrusted collaborative environments. Early research included P4QS.

[23] Pseudo-anonymous authentication and cryptographic mechanisms are employed to resist speculative attacks and ensure the traceability of query behavior. Subsequently, researchers have gradually expanded their focus to trusted verification and behavioral constraint mechanisms in untrusted environments. The immutability and auditability of blockchain are used to record the behavior of collaborating nodes to identify malicious nodes and assess trustworthiness.

[24] Zhang et al.

[25] Ren continuously assesses reputation and constrained behavior through on-chain dynamic reputation evaluation or collaborative trust evidence reasoning. Furthermore, SECRECY

[26] iQuery optimizes privacy protection through multi-party secure computation, ensuring data isolation and providing zero information leakage protection.

[27] By using smart contract mechanisms to prevent collusion, the reliability, flexibility, and scalability of queries are improved. (BCV-ReID)

[28] By combining blockchain with vehicle re-identification technology, a collaborative consensus mechanism is used to verify the collaborative behavior of multiple nodes, addressing the collaborative verification problem in low-trust environments. Although existing methods have made progress in collaborative trustworthiness and privacy protection, they lack verifiable constraints on collaborative interactions and local decision-making. Summary of the Invention

[0010] To address the aforementioned challenges, this invention constructs a blockchain-enhanced DHT Network (BDN) to support privacy-preserving multimodal mobile queries. In mobile query scenarios, the query request is initiated by the mobile user and relies on the covert collaboration of nearby users to complete, thereby concealing the identity of the queryer and resisting collusion attacks. Specifically:

[0011] A covert mobile query method based on blockchain and distributed hash table is proposed. First, a blockchain-enhanced distributed hash table (DHT) network (BDN) is constructed. Then, in the query scenario under BDN, the mobile user initiates the request and completes it through covert cooperation of adjacent users, thereby hiding the identity of the queryer and resisting collusion attacks.

[0012] The BDN framework consists of: the blockchain serving as the backbone network, undertaking the core functions of transaction processing and smart contract execution; the DHT network used to store users' geographic information to support mobile user queries; and user-anonymous collaboration and behavior verification for queries running on the sidechain and main chain networks, respectively.

[0013] The blockchain data layer accesses user information in the DHT network through indexing and storage interfaces; the message types in the P2P communication protocol of the blockchain network layer are expanded to support information transmission in the DHT network; full nodes in the blockchain assume the function of storage nodes in the DHT network; smart contracts ensure the security of DHT access.

[0014] This invention presents a user collaboration privacy protection framework based on BDN, combining the decentralized trust of blockchain with the adaptability of DHT to dynamic nodes. Key information is stored on the blockchain to ensure security, while active data is stored in DHT to improve access speed. The framework internally employs MPT to optimize retrieval speed and storage efficiency, and dynamically manages the user data lifecycle through smart contracts.

[0015] To achieve secure intra-group interaction, an invalid transaction propagation mechanism combining adaptive compression and ring signature technology was designed to enable covert communication between collaborative members. This significantly improves the privacy and efficiency of communication while reducing communication costs and message compression losses.

[0016] Behavioral verification mechanisms are used to incentivize users to participate in group collaboration efficiently and honestly, maintaining a healthy and trustworthy collaborative ecosystem.

[0017] Security analysis demonstrates the effectiveness of the proposed framework in terms of privacy protection and data security; simulation results confirm that the proposed framework reduces communication and time costs compared to benchmark methods.

[0018] This invention mainly includes two contributions:

[0019] Considering the frequent access and dynamic changes in connection status of mobile users, this invention leverages the decentralized trust of blockchain and the support of DHT for decentralized query indexing and rapid location to manage mobile query data in a differentiated manner. Key data is uploaded to the blockchain to ensure integrity and traceability, while active data is stored in the DHT to reduce access latency. This invention introduces Merkle Patricia Tree (MPT) to improve data retrieval and storage efficiency, and uses smart contracts to manage the lifecycle of user data.

[0020] To avoid main chain network congestion caused by large-scale query services, a covert communication framework for main chain and side chain collaboration was designed to decouple high-frequency collaboration from covert interactions. Side chain operations are built on a neighbor connection protocol, employing DHT and invalid transaction diffusion to construct covert collaboration, and utilizing adaptive plaintext compression and ring signatures to enhance message embedding and privacy protection. The main chain verifies and traces collaborative lines through smart contracts to prevent unauthorized access.

[0021] Security analysis shows that the proposed method possesses identity non-linkability and invisibility, effectively protecting user query privacy and preventing forgery and collusion attacks, thus ensuring the security of identity and content. Simulation results confirm that the proposed scheme outperforms benchmark methods in terms of storage efficiency, cost-effectiveness, real-time performance, and security. Attached Figure Description

[0022] Figure 1 This represents a collaborative hidden query framework on BDN;

[0023] Figure 2 This refers to user-data-based access and updates within the DHT network;

[0024] Figure 3 This represents the MPT maintained by storage node B;

[0025] Figure 4 This indicates the spread of invalid transactions based on DHT on the sidechain;

[0026] Figure 5 This indicates user behavior verification and tracing;

[0027] Figures 6(a) and 6(b) represent query efficiency and storage space utilization, respectively.

[0028] Figure 6(a) shows the query time.

[0029] Figure 6(b) shows the storage space utilization rate;

[0030] Figure 7 Indicates query latency for different query content sizes;

[0031] Figures 8(a) and 8(b) illustrate the security and time cost of covert communication, respectively.

[0032] Figure 8(a) shows the number of times the data was backtracked.

[0033] Figure 8(b) shows the time cost;

[0034] Figure 9 This indicates the query failure rate for different privacy protection schemes;

[0035] Figures 10(a) to 10(c) illustrate query security under different collaboration modes, where:

[0036] Figure 10(a) shows 10% of malicious nodes.

[0037] Figure 10(b) shows 20% malicious nodes.

[0038] Figure 10 (c) shows 30% of malicious nodes. Detailed Implementation

[0039] 1 Overview

[0040] Mobile queries, especially multimodal data interactions, face privacy risks in dynamic network environments. Even if the query content is encrypted, communication patterns and collaborative relationships can still be exploited by malicious observers, exposing user identities and behavioral characteristics.

[0041] To support privacy-preserving multimodal queries in untrusted environments, this invention constructs a blockchain-enhanced Distributed Hash Table (DHT) network, BDN. In query scenarios, requests are initiated by mobile users and completed through covert collaboration among neighbors, thus concealing the identity of the queryer and resisting collusion attacks. The proposed framework combines the decentralized trust of blockchain with the adaptability of DHT to dynamic nodes, ensuring the integrity and traceability of critical data and offloading frequently accessed data from the chain to reduce query latency. Based on this, a Merkle Patricia Tree (MPT) is introduced to optimize data indexing and retrieval, and smart contracts are used for automated management of the data lifecycle. To support large-scale mobile queries, this invention designs a covert communication framework for collaboration between the main chain and side chains, decoupling high-frequency collaboration and covert interaction. The side chains achieve covert collaboration based on DHT and an invalid transaction diffusion mechanism, and enhance privacy through adaptive plaintext compression and ring signatures; the main chain verifies and traces collaborative behavior through smart contracts, ensuring system security and controllability.

[0042] Theoretical analysis shows that the method proposed in this invention possesses identity unlinkability and communication invisibility, effectively protecting user query privacy and resisting forgery and collusion attacks. Simulation results further verify that the proposed scheme outperforms benchmark methods in terms of storage efficiency, economic cost, real-time performance, and security.

[0043] The following section is arranged as follows:

[0044] Section 2 describes the BDN architecture, mobile query framework, design goals, and threat model.

[0045] Section 3 introduces mobile user data management based on BDN.

[0046] Section 4. Constructing a collaborative, covert communication mechanism on the sidechain.

[0047] Section 5 Design of behavior verification and data protection scheme on the main chain.

[0048] Section 6 evaluates the performance of the proposed scheme through simulation and numerical analysis.

[0049] 2. BDN Model

[0050] Consider a case such as Figure 1 The illustrated blockchain-assisted mobile query scenario protects the privacy of queryers through covert collaboration between neighboring users. In this architecture, the blockchain serves as the backbone network, handling core functions such as transaction processing and smart contract execution. An MPT-enhanced DHT network stores users' geographic information to support large-scale mobile queries. User covert collaboration and behavior verification for queries operate on sidechains and the main chain, respectively, to balance real-time performance, security, and scalability.

[0051] like Figure 1 As shown, the BDN framework of this invention adopts a loosely coupled design. The blockchain data layer accesses user information in the DHT network through indexing and storage interfaces. The message types in the P2P communication protocol of the blockchain network layer are extended to support information transmission in the DHT network. Full nodes in the blockchain assume the function of DHT network storage nodes. Smart contracts ensure the security of DHT access.

[0052] 2.1 Role Division

[0053] Collaborative covert query mechanisms are used to prevent untrusted service provider (SP) snooping and speculation about user privacy. Figure 1 In China, mobile users are divided into three roles:

[0054] 1) Query the initiator ( Figure 1 middle When running the query service, its query messages are covertly passed to collaborators.

[0055] 2) Query the forwarder ( Figure 1 middle ), selected from the DHT by the smart contract, to help Forward the query results to the SP;

[0056] 3) Result returner ( Figure 1 middle Similarly, it is selected by the smart contract and is responsible for receiving messages from the SP and sending them to... Return the query results.

[0057] 2.2 Sidechain and Mainchain Collaboration Framework

[0058] Within the proposed BDN framework, the main chain and side chains achieve secure information exchange through a two-way anchoring mechanism. The side chains, acting as an additional network, connect to user devices via lightweight clients. To avoid congestion on the main chain network, covert communication during collaboration is migrated to the side chains. The admission mechanism on the side chains helps block potential external attackers and enhances the privacy and untraceability of user queries. The main chain is responsible for verifying and tracing user behavior, ensuring the reliability and security of the collaborative query process. This collaborative framework balances privacy protection and service provision.

[0059] 2.3 Threat Model

[0060] Assume there are three types of malicious entities in the scenario:

[0061] Suspicious SPs: 1) Speculative attack: SPs may leak and speculate on the privacy information of the queryer based on the user's prior knowledge; 2) Collusion attack: SPs may have two collaborators provide information about the queryer's privacy.

[0062] Malicious eavesdroppers: Eavesdroppers on the blockchain can identify the communication relationships, real identities, and locations of collaborating users through traffic analysis and transaction correlation.

[0063] Untrustworthy collaborators: 1) Selfishness: Forwarders may claim they have not received the task from the querier; result returners may falsely claim they have not received the SP's query results, or fail to respond after receiving the query results; 2) Spreading false results: Forwarders and returners may submit incorrect queries or even return incorrect results.

[0064] 2.4 Design Objectives

[0065] The solution of this invention should achieve the following design objectives:

[0066] Identity privacy: 1) Unpredictability: It is difficult for attackers to infer the user's real identity through the user information recorded in the DHT; 2) Invisibility: The user's real identity and location are not visible to the forwarder and the SP.

[0067] Query privacy: Attackers and service providers find it difficult to infer the real queryer by observing the queries submitted by collaborators, and they are even less able to associate the privacy preferences related to the query information with real users.

[0068] Stealth: Attackers cannot perceive the communication between collaborators, making it difficult to obtain user privacy by intercepting and analyzing intra-group communication.

[0069] Trustworthiness: Any abnormal or irregular behavior by users during the collaboration process will be recorded and traced, thereby regulating user behavior.

[0070] Robustness: Even if the number of candidate collaborators is insufficient, the proposed solution still has the ability to protect privacy.

[0071] Multimodal: In addition to traditional text message queries, the proposed framework can support rich media queries.

[0072] 3. Mobile User Management

[0073] Users whose query intervals are less than a given threshold are considered active. Active users' key-value pairs are stored in the DHT network. Using a Consistent Hashing Function (CHF), user key-value pairs are uniformly and orderly mapped to DHT storage nodes on a hash ring, where similar data is clustered at adjacent nodes. User geolocation is converted into a geographic key. The similarity between key-value pairs is positively correlated with the distance between users. Each user's geographic key is unique and maps to a data entry stored in the DHT. Due to the irreversibility of the hash function, even if a malicious entity obtains the geographic key, it cannot deduce the user's true location. Inside the DHT node, an MPT (Multi-Level Mapping) is used to construct the data access structure to improve retrieval efficiency and storage utilization. The user's public key address is used to establish a mapping between the DHT and the blockchain network's information storage, providing anonymity for the user.

[0074] 3.1 User Information Initialization

[0075] To expedite location matching in queries, user information is mapped to the storage node closest to their geographic key. A user joining or leaving only results in minor data updates for the neighboring region, avoiding network-wide data migration and routing updates.

[0076] by Figure 2 Explained by example Initialization in DHT. The blockchain address (pseudonym) and real geographical location are represented as and .make represent The geographic key obtained after inputting CHF. When joining the DHT network for the first time, he first connects to a bootstrap node and then... and register( Figure 2 (Step 1-1 in the process). Based on proximity, this guiding node will... The geographic key is mapped to the nearest storage node A ( Figure 2 (Steps 1-2 in the original text). Node A creates an MPT containing only the root node. The mapping relationship on the blockchain side is bound to the root node, which is used to associate the DHT storage and the blockchain contract. The key-value pairs are stored in the MPT, becoming a DHT node.

[0077] The following explains the creation of MPT. Assume... Figure 2 The five user key-value pairs stored in node B are constructed as follows Figure 3 An MPT in the middle. Hash pointers are used to chain users with similar geographic keys ( and These users are created as adjacent leaf nodes to speed up queries. In the MPT construction, compressed node paths and reused storage space reduce the modifications to the tree structure caused by data changes. The structure only records user nodes containing data, avoiding empty nodes. Merkle proofs can be used to verify the integrity of user key values ​​in the MPT.

[0078] 3.2 User Geographic Key Update

[0079] Based on the uniqueness of the hash function, a change in a user's location will trigger an update of the geokey. Figure 2 middle Take the change of position as an example. When he moves from position... Move to At that time, storage node A put Modified to . The information is remapped by node A via the DHT protocol and replicated to other storage nodes in the network. Will Synchronize with the original storage node A ( Figure 2 Step 2-1). Node A according to Extract from MPT And generate a signature for the updated information. According to , The key-value pairs are remapped by node A to storage node B, which is closer to it. Figure 2 Step 2-2 in the process. Node B receives... And verify the signature. and It is inserted into an MPT maintained by node B. Figure 2 Steps 2-3 in the original storage node A are used. cover However, one update record will be retained. This is used to guide subsequent query requests. Here, T1 represents... Status information (i.e., timestamp).

[0080] 3.3 User Information Acquisition

[0081] The retrieval of user information in the DHT is overseen by smart contracts. These smart contracts utilize the Chord protocol.

[29] Search and return the results. Figure 2 As shown, the smart contract is Retrieve user information with similar geographic keys. Access request received ( Figure 2 In step 3-1), the smart contract will contain DHT routing lookup information is disseminated among DHT nodes. Based on the proximity of geographical keys, [the information is distributed]... The nearest storage node B was quickly located. Figure 2 Step 3-2). Within the MPT maintained by B, based on the concatenated hash pointers, the distance... The most similar user is and Based on the data stored in the root node and Other user information can be retrieved.

[0082] 4. Covert collaboration on side chains

[0083] In collaborators and Despite the obstruction, the inquirer While invisible to SPs, malicious eavesdroppers can still observe communications associated with collaborators.

[31] To track While blockchain-based covert communication can evade such tracking, it relies on special transactions to carry secret messages. These special transactions require network-wide broadcasting and consensus, making it difficult to support collaborative queries with low latency and large data volumes.

[0084] To address the aforementioned issues, this section designs a covert collaboration mechanism that runs on a sidechain and integrates DHT and invalid transaction propagation. Figure 4 By sender Deliver plaintext message M to the receiver. For example, the processing flow between the sender and receiver is presented at the point-to-point interaction level:

[0085] A. Steganography and Content Diffusion (at the sender) )

[0086] 1) M is compressed into I to balance transmission cost and quality loss (see Section 4.1).

[0087] 2)I was Encrypted as E, where The recipient's public key;

[0088] 3) E is embedded in an invalid transaction, and the invalid transaction carrying E is represented as TX;

[0089] 4) The ringed signature is To prevent backtracking by collaborators (see Section 4.2).

[0090] After completing the above steps, The connection protocol is delivered to lower-level neighboring nodes. This protocol is provided on demand. Configure a set of neighboring nodes that include collaborators to obscure the spread of invalid transactions (see Section 4.3).

[0091] in, This represents the signature value of the ring signature.

[0092] B. Content Reception and Retrieval (at the receiving end) )

[0093] 1) According to TX received party It is identified and received, but discarded by other nodes;

[0094] 2) from Extract E from it;

[0095] 3) E is the private key Decrypted as I;

[0096] 4) I is decompressed into M.

[0097] 4.1 Plaintext Adaptive Compression

[0098] On a blockchain network, the embedding capacity of a transaction determines the query services that covert communication can support. For multimedia queries, due to the high requirement for embedding capacity, some transaction construction schemes, such as...

[32] and

[35] Even using invalid transactions of the multiple-input multiple-output type is insufficient to accommodate a complete query. Splitting a query into multiple independent transactions increases query latency and transaction fees. To address this, an adaptive query compression strategy is designed to reduce query latency, economic costs, and quality loss simultaneously.

[0099] Assume the capacity of a single transaction is Q, and the total volume of plaintext to be transmitted is V. The number of transactions required to directly embed the unprocessed plaintext is calculated as follows:

[0100] (1)

[0101] Let 'a' represent the minimum acceptable compression ratio. For queries that cannot be compressed, 'a' is set to 1; for compressible content (such as images), 'a' is set to 1. The number of transactions required to achieve the lowest compression rate for plaintext is...

[0102] (2)

[0103] Given The cost of compression was calculated as

[0104] (3)

[0105] Where c is the time cost factor for compressing each unit of query content. The compression ratio is adjusted to minimize the number of transactions.

[0106] (4)

[0107] This avoids quality loss due to excessive compression without increasing transmission latency and transaction costs.

[0108] 4.2 Anti-backtracking strategy

[0109] Malicious collaborators can observe the transaction reception of neighboring nodes (e.g., whether there is a pattern or similarity in transaction patterns).

[33] Obtaining sensitive information about the sender (such as behavioral patterns or communication habits)

[34] And inform SP. To counter such collusion, a lightweight ring signature scheme was introduced.

[0110] The set of public keys of the ring signature members constructed by the sender is represented as follows: Each public key in set R corresponds to one member. The sender uses... right Ring Signature [36,38] ,get After receiving a transaction, if the recipient... If the signature is valid, ignore it; otherwise, verify the validity of the signature, and discard any transactions that fail.

[0111] The proposed strategy considers the following covert communication between two pairs of nodes:

[0112] 1) and between: Possibly in collusion with SP to obtain information about Privacy information about one's real identity. During the ring signature generation process, The sender's public key is included in the ring member set. Since the receiver is not directly involved in ring signing, they cannot identify the sender. Even if the private keys of other members in the ring are leaked, the sender's identity remains undiscovered, ensuring anonymity in communication.

[0113] 2) and Between: as the recipient No backtracking collaborators The conditions. Regarding The ring signature is omitted, and the query is delivered as an invalid transaction. .

[0114] 4.3 Neighbor Connection Configuration

[0115] Node connections on a blockchain network are determined by a neighbor protocol. Neighbor connection protocols on sidechains offer greater openness and customizability compared to those on public chains. This paper aims to improve the neighbor connection protocol on sidechains to support on-demand neighbor configuration based on DHT.

[0116] by Figure 4 For example, the neighbor protocol on the sidechain obtains user information from the DHT according to the method in section 3.3. The user whose geographic key differs least from the queryer's was selected. Collaborators. Other similar users ( Also as To confuse the neighbors In fact, due to their geographical proximity, users in DHT are likely to be... First-level neighbor nodes require no intervention from the neighbor protocol. Nodes in the DHT are temporarily set as neighbors only when there are insufficient neighbors or when collaborators are not first-hop neighbors, to obfuscate the attacker's observation. These settings are transparent to the user. Even if the true recipient is discovered by the attacker, it is extremely difficult to reverse-engineer the sender's privacy.

[0117] Collaborator information can be temporarily cached and dynamically updated by the smart contract. If there are not enough candidate collaborators in the DHT (less than 2), the collaboration method will be adjusted accordingly. If there is only one collaborator, that collaborator acts as both a forwarder and a returner, and the queryer interacts with the SP under its cover. If no collaborator is available, the queryer interacts with the SP using the public key address as a pseudonym.

[0118] 4.4 Security Analysis

[0119] Theoretically analyze the achievement of the proposed covert collaboration goals.

[0120] Identity non-linkability: 1) Because the user query POI is separated from the user ID, and the user ID is separated from the user location, the SP cannot associate the query with the real query user; 2) Collaborative covert communication can prevent external malicious eavesdroppers from discovering special transactions carrying secret information among numerous transactions.

[0121] Identity Invisibility: 1) The ring signature technology used in the proposed covert communication will prevent the sender from being detected. Hidden among the many signatories, making For the communication recipient, the true identity Invisible; 2) Forwarder to replace one's own identity Submitting a query to the SP makes The identity is not visible to SP.

[0122] Identity unpredictability: The user information stored in DHT storage nodes does not involve the user's real identity privacy. Even if an attacker obtains the information in the DHT, it is very difficult to deduce the user's real identity and location.

[0123] Anti-collusion attack: Protected by ring signatures, the queryer... The true identity of SP is difficult for collaborators to trace back, and even if SP and the two collaborators conspire, it cannot be confirmed. His true identity.

[0124] The unforgeability of identity and content: 1) The smart contract is used to retrieve user information, and the resulting set of candidate collaborators is unforgeable; 2) In order to obtain the service, the querier u has no reason to forge its collaborators and query content.

[0125] 5. Behavioral verification on the main chain

[0126] A user behavior verification and tracing scheme has been developed, running on the main chain and working in conjunction with side chains through covert communication. Figure 5 For example, covert communication (red dotted line) is used only for query forwarding and result return between collaborators. Encryption and hashing are used to protect and verify collaboration against selfish and malicious behavior.

[0127] 1) Before sending the query, Encrypt the query using SP's public key K. and hash value And query task Send discreetly . yes public key, yes The blockchain address.

[0128] 2) signature Used to verify Legitimate identity, encrypted query It was sent to the SP.

[0129] 3) SP decryption query, through Make sure this task is a new task that has never been submitted before.

[0130] 4) SP according to the return address Will Send to . Indicates the use of a public key Encrypted query results The hash value representing the query result. This represents the recalculated query hash value.

[0131] 5) The received results are delivered covertly to .

[0132] 6) Verify the SP's signature Confirm if the result comes from SP. To verify... and Whether the forwarding and return tasks were completed in a standardized manner. Use private key Decrypt the result and calculate its hash value. Compare it with the returned result. This verifies whether the data has been tampered with. (Verification successful) Correctness, Confirmed Did you honestly complete the forwarding?

[0133] After completing a collaboration, if confirm and To legally complete the forwarding and return tasks, he must submit a settlement confirmation for the query task to the smart contract within the specified time. Upon receiving the confirmation, and Users whose reputation score is boosted are considered to have engaged in malicious behavior, and their reputation score is deducted. Users with reputation scores below the threshold are blacklisted and punitively prohibited from participating in collaborations. The smart contract's judgment results and DHT operations are stored on-chain for later review.

[0134] 6. Simulation Experiments and Numerical Analysis

[0135] Simulation experiments were used to verify the proposed BDN, intra-group covert communication, and collaborative privacy protection scheme in terms of query and storage efficiency, compression latency and cost, transmission delay, and security.

[0136] 6.1 Query and Storage Efficiency Analysis of BDN

[0137] This experiment compares the performance of different distributed data storage schemes. Three typical data storage schemes were selected as benchmarks: 1) data is stored on a blockchain network (named Blockchain); 2) data is stored on a DHT network (named DHT); and 3) DHT nodes use a Merkle Tree (MT) structure to organize user data (named MT-DHT). A simulated network containing 100 nodes was built on a server using Docker containers to test storage performance under different data scales.

[0138] 10,000 randomly generated query requests were injected into the network to measure query latency and storage utilization under different DHT storage sizes. As shown in Figure 6(a), the proposed scheme consistently maintained the lowest query latency. This is attributed to the efficient data organization and retrieval methods of MPT. Based on geographical location, query requests can be quickly located to the relevant data branches. Suboptimal DHT and MT-DHT rely on the routing protocol of the DHT network to direct query requests to the nodes storing the target data. Since each query must traverse all nodes, the query latency of Blockchain is higher than that of DHT and increases with the number of nodes.

[0139] Storage utilization is defined as the ratio of actual storage usage to the baseline storage space. For fairness, the storage space is uniformly set at 10GB. As shown in Figure 6(b), the storage utilization of the proposed scheme shows a slow downward trend with the increase of data size, but always remains above 90%. When using the Blockchain method, each node needs to store a complete copy of the data, and the storage utilization efficiency hovers around 60%. Although DHT and MT-DHT can achieve storage utilization of 72% to 83% when the data size is small, their performance drops significantly when the data size is large due to the storage of data copies. Benefiting from the node path compression and storage space reuse of MPT, the proposed scheme achieves the highest level of storage utilization and stability.

[0140] 6.2 Delay and Economic Cost Analysis of Adaptive Compression

[0141] This experiment evaluates the practicality of the proposed plaintext compression in terms of time cost (compression and transmission latency) and economic cost (transaction fees). The Elements technique was used to build a sidechain on the Bitcoin test network. Multiple invalid transactions were constructed to support subsequent covert communication. All experimental transactions were set to single-input, single-output. The maximum embedding capacity of a single transaction was 1376 bits.

[0142] Table 1 lists the time and economic costs of different compression schemes when embedding 10,000 bits of information. Regardless of the scheme used, compression ratio, transmission latency, and transaction fees are positively correlated. Although the proposed scheme introduces a compression latency of 0.020 seconds, its advantage in transmission latency keeps the overall latency at a minimum. Compared to a scheme with a compression ratio of 50%, the proposed scheme reduces transaction fees by 73.5 sat.

[0143] Table 1. Comparison of time and economic costs under different compression schemes

[0144] Plaintext processing scheme Size after compression (bits) Compression delay (s) Transmission delay (s) Transaction volume Transaction fees (SAT) <![CDATA[Direct embedding

[39] > 10000 0 21.6 8 196 Compression rate 20% 8000 0.005 15.1 6 147 Compression rate 50% 5000 0.010 10.4 4 98 Compression rate 80% 2000 0.015 4.8 2 49 Adaptive compression 1376 0.020 2.3 1 24.5

[0145] Next, we will examine the impact of queries with different data volumes on latency. Figure 7 Although query latency and query latency are positively correlated, the rate of increase varies significantly among different solutions. Due to its disadvantage in transaction volume, the direct embedding solution exhibits the most pronounced latency growth trend. Fixed compression offers almost no room for latency optimization when dealing with large-volume queries. Under the proposed solution, transmission latency and compression cost achieve an optimal balance, demonstrating superior performance in terms of both time and economic costs.

[0146] 6.3 Analysis of the anti-backtracking properties and communication delay of covert communication

[0147] This section compares the backtracking resistance and overall communication latency of different communication schemes. Ordinary communication (named NC) and literature...

[35] The covert communication scheme (named TFF-CC) was selected as the baseline method. In the constructed sidechain network environment, attackers attempted to identify invalid transactions carrying secret information and track the true identity of the queryers through traffic analysis and message eavesdropping.

[0148] The number of times the sender is traced back is used to evaluate the anti-traceback effect. In Figure 8(a), thanks to the unconditional anonymity of the ring signature, the number of tracebacks under the proposed method remains at a low level. When running TFF-CC, the receiver can identify the sender's identity by analyzing the communication patterns of neighboring nodes, increasing the risk of identity exposure.

[0149] The time cost of covert communication includes the delay from transaction construction to plaintext message reception. This time cost is positively correlated with the number of communications, as shown in Figure 8(b). Without introducing additional security mechanisms, the overall communication time increases slowly when directly transmitting plaintext messages using NC. Nevertheless, NC is vulnerable to traffic analysis and message eavesdropping by network attackers. The total communication time of TFF-CC is higher than that of NC due to the high cost of constructing the special transaction phase. Due to the introduction of ring signatures and adaptive compression, the proposed scheme's total communication time is slightly higher than the baseline scheme, achieving a significant improvement in security at the cost of a small time investment. Adjusting the frequency and data volume of covert communication can balance security and efficiency.

[0150] 6.4 Robustness Analysis of Collaborative Queries

[0151] This subsection constructs a distributed query system integrating blockchain and DHT, and delineates a local area within it for simulating collaborative user queries. Within this area, the proportion of malicious nodes among users is set to varying values. Query requests are sent to selected collaborators and Service Providers (SPs). SPs process the query requests and return results to the collaborators. Malicious nodes may engage in behaviors such as providing false responses, denial of service, or attempting to infer user query content and identity. At different node scales, 100 random queries are continuously sent to statistically analyze the interception or failure to obtain correct query results for users.

[0152] Based on TTP

[40] and k-anonymous

[41] The query was selected as the baseline method. The proportion of malicious nodes in the network was set at 30%. The query failure rate was positively correlated with the number of users. Figure 9 As shown, an attack or malfunction of a TTP node can affect the entire system, resulting in the highest query failure rate among all solutions. The query failure rate of k-anonymity is lower than that of the TTP solution, but still higher than that of the proposed solution. k-anonymity struggles to effectively defend against interception and speculation attacks targeting query content when facing a large number of malicious nodes. The proposed solution exhibits the lowest query failure rate across various user numbers.

[0153] Next, we examine the impact of collaboration modes on query security. Figures 10(a) to 10(c) The query failure rate is positively correlated with the proportion of malicious nodes. The query failure rate increases fastest under no-cooperation conditions. Malicious nodes can easily hide themselves in large-scale networks. The query failure rate shows an upward trend as the number of users increases. Introducing cooperation can improve query security, especially when the proportion of malicious nodes is high. Under the three malicious node proportions, the query failure rate under two-cooperator conditions remains below 15%, at the lowest level. Regardless of network size or threat level, the proposed solution maintains a stable query success rate.

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Claims

1. A covert mobile query method based on blockchain and distributed hash tables. Its features are as follows: First, a blockchain-enhanced distributed hash table (DHT) network (BDN) is constructed; then, in the query scenario under BDN, the mobile user initiates the request and completes it through the covert cooperation of adjacent users, thereby hiding the identity of the queryer and resisting collusion attacks. The BDN framework consists of: the blockchain serving as the backbone network, undertaking the core functions of transaction processing and smart contract execution; the DHT network used to store users' geographic information to support mobile user queries; and user-anonymous collaboration and behavior verification for queries running on the sidechain and main chain networks, respectively. The blockchain data layer accesses user information in the DHT network through indexing and storage interfaces; the message types in the P2P communication protocol of the blockchain network layer are expanded to support information transmission in the DHT network; full nodes in the blockchain assume the function of storage nodes in the DHT network; smart contracts ensure the security of DHT access. In query scenarios under BDN: a. User roles are divided as follows: 1) Query the initiator When running the query service, The query message was secretly passed to the collaborator; 2) Query forwarders Selected from the DHT by a smart contract to help Forward the query to the service provider (SP); 3) Result returner Selected by the smart contract, it is responsible for receiving messages from the SP and sending them to... Return the query results; Collaborators include and ; b. Covert communication between sidechains and mainchain: Sidechains serve as an additional network, connecting to user devices via lightweight clients; covert communication during collaboration is migrated to the sidechain for execution. The sidechain achieves covert collaboration based on DHT and invalid transaction propagation mechanism. It realizes covert communication between collaborative members through the invalid transaction propagation mechanism of adaptive plaintext compression and ring signature method. The main chain verifies and tracks user collaboration behavior through smart contracts; c. Malicious entities in the threat model include: A suspicious service provider (SP) is characterized by: the possibility that the SP may infer the queryer's private information based on the user's prior disclosure; and the possibility that the SP may obtain information about the queryer's privacy from two collaborators. Malicious eavesdroppers are characterized by their ability to identify the communication relationships, true identities, and locations of collaborating users through traffic analysis and transaction correlation. Untrusted collaborators are characterized by: the possibility that the forwarder may claim not to have received the task from the querier; the possibility that the result returner may falsely claim not to have received the query result from the SP, or may not respond after receiving the query result; and the possibility that both the forwarder and the returner may submit incorrect queries or even return incorrect results.

2. The covert mobile query method based on blockchain and distributed hash table as described in claim 1, characterized in that: The user management method is as follows: users whose query interval is less than a given threshold are identified as active users, and their key-value pairs are stored in the DHT network; Through the consistent hash function CHF, active user key-value pairs are uniformly and orderly mapped to DHT storage nodes on the hash ring, where similar data is clustered at adjacent nodes; User geographic locations are converted into geographic keys; the similarity between key values ​​is positively correlated with the distance between users; each user's geographic key is unique and maps to a data entry stored in the DHT; Inside the DHT node, the Merkle prefix tree (MPT) is used to construct the data access structure; A user's public key address is used to establish a mapping between the DHT and the blockchain network information storage, providing anonymity for the user.

3. The covert mobile query method based on blockchain and distributed hash table as described in claim 2, Its features include user management, which includes: a. User information initialization User information is mapped to the storage node closest to their geographic key; Inquiry Initiator Initialization in DHT: The blockchain address and real geographical location are represented as follows: and ;make represent The geographic key obtained after inputting CHF; When joining the DHT network for the first time, he first connects to a bootstrap node and then... and Register; based on proximity, this bootstrap node will The geographic key is mapped to the nearest storage node A; node A creates an MPT containing only the root node; the mapping relationship on the blockchain side is bound to the root node to associate the DHT storage and the blockchain contract. The key-value pairs are stored in the MPT, becoming a DHT node; b. User geokey update A change in user location triggers an update of the geokey; when From position Move to At that time, storage node A will store the geographic key. Modified to ; The information is remapped by node A via the DHT protocol and replicated to other storage nodes in the network; Will Synchronize with the original storage node A; node A according to Extract from MPT And generate a signature for the updated information; based on , The key-value pairs are remapped by node A to the more nearby storage node B; node B receives... And verify the signature; and It was inserted into an MPT maintained by node B; the original storage node A used cover And retain one update record. This is used to guide subsequent query requests; where T1 represents Status information; c. Obtaining User Information The retrieval of user information in the DHT is overseen by smart contracts; smart contracts use the Chord protocol to search for and return results. Smart contracts are Retrieve user information with similar geographic keys; upon receiving an access request, the smart contract will contain... DHT routing lookup information is disseminated among DHT nodes; based on the proximity of geographical keys, it is distributed among… The nearest storage node B is quickly located; within the MPT maintained by B, the distance is determined based on the concatenated hash pointers. The most similar user is and Based on the data stored in the root node and Other user information was retrieved.

4. The covert mobile query method based on blockchain and distributed hash table according to claim 3, characterized in that: The method for creating an MPT is as follows: Assume that multiple user key-value pairs stored in node B are constructed into an MPT; Hash pointers are used to chain users with similar geographic keys, and these users are created as adjacent leaf nodes; In the MPT structure, compressed node paths and reused storage space reduce the modification of the tree structure by data changes. This structure only records user nodes containing data; Merkle proofs are used to verify the integrity of user key values ​​in the MPT.

5. The covert mobile query method based on blockchain and distributed hash table according to claim 1, characterized in that: Covert collaboration on the sidechain is a covert collaboration that operates on the sidechain and integrates DHT and invalid transaction propagation; based on this covert collaboration communication method, Deliver plaintext message M to the receiver. The processing procedure is as follows: A. At the sender Steganography and content diffusion: A-1)M is compressed into I to balance transmission cost and quality loss; A-2)I was Encrypted as E, where The recipient's public key; A-3)E is embedded in an invalid transaction, and the invalid transaction carrying E is represented as TX; A-4) The ringed signature is To prevent collaborators from retaliating; among them, This represents the signature value of the ring signature; at last, The connection protocol delivered to lower-level neighbor nodes is on demand. Configure a set of neighboring nodes that include collaborators to obscure the spread of invalid transactions; B. At the receiving party Content reception and retrieval: B-1) According to TX received party It is identified and received, but discarded by other nodes; B-2) from Extract E from it; B-3)E is the private key Decrypted as I; B-4)I is decompressed into M.

6. The covert mobile query method based on blockchain and distributed hash table according to claim 5, characterized in that: In covert collaboration on sidechains: a) Plaintext adaptive compression Assuming the capacity of a single transaction is Q, and the total volume of plaintext to be transmitted is V; the number of transactions required to directly embed unprocessed plaintext is calculated as follows: (1) Let 'a' represent the minimum acceptable compression ratio; for queries that cannot be compressed, 'a' is set to 1; for compressible content, ... The number of transactions required to achieve the lowest compression rate for plaintext is: (2) Given The cost of compression was calculated as (3) Where c is the time cost factor for compressing each unit of query content; The compression ratio was adjusted to minimize the number of transactions. (4) b. Anti-backtracking strategy Introducing a lightweight ring signature method: The set of public keys of the ring signature members constructed by the sender is represented as follows: Each public key in set R corresponds to one member; The sender uses its public key right Ring signature, obtained Upon receiving a transaction, if the recipient discovers its public key... If the signature is valid, ignore it; otherwise, verify the validity of the signature, and discard transactions that fail verification. c. Neighbor connection configuration Improve the neighbor connection protocol on the sidechain to support DHT-based on-demand neighbor configuration, as follows: The neighbor protocol on the sidechain retrieves user information from the DHT; As a queryer The user with the smallest difference in geographic key was selected as Collaborators, and other similar users also act as... To confuse the neighbors When there are insufficient neighbors or the collaborator is not a one-hop neighbor, the node in the DHT is temporarily set as a neighbor. The collaborator's information is temporarily cached and dynamically updated by the smart contract; if the number of candidate collaborators in the DHT is less than 2, the collaboration method is adjusted accordingly; if there is only 1 collaborator, the collaborator is both a forwarder and a returner, and the queryer interacts with the SP under its cover; if there is no available collaborator, the queryer interacts with the SP using the public key address as a pseudonym.

7. The covert mobile query method based on blockchain and distributed hash table according to claim 6, characterized in that: The user behavior verification and tracing method on the main chain runs on the main chain and works in conjunction with the covert communication of the side chain; the covert communication is only used for query forwarding and result return between collaborators; Encryption and hashing are used to protect and verify collaboration; The main chain's user behavior verification and tracing steps include: S1) Before sending the query Encrypt the query using SP's public key K. and hash value And query task Send discreetly ; yes public key, yes The blockchain address; S2) signature Used to verify Legitimate identity, encrypted query Sent to SP; S3)SP decryption query, through Make sure this task is a new task that has never been submitted before; S4)SP according to the return address Will Send to ; Indicates the use of a public key Encrypted query results The hash value representing the query result. This represents the recalculated query hash value; S5) The received results are delivered covertly to ; S6) Verify the SP's signature To confirm and verify if the result comes from SP, the method is as follows: To verify and Whether the forwarding and return tasks were completed in a standardized manner. Use private key Decrypt the query result and calculate its hash value, then compare it with the returned result. To confirm whether the data has been tampered with; through verification Correctness, confirm Did you honestly complete the forwarding? After completing a collaboration, if confirm and If the forwarding and return tasks are completed legally, a settlement confirmation for the query task will be submitted to the smart contract within the specified time; upon receiving the confirmation, and Users whose reputation score is increased are considered to have engaged in malicious behavior, and their reputation score is deducted. Users whose reputation score is below the threshold are blacklisted and punitively prohibited from participating in collaboration. The judgment results of smart contracts and operations on DHT are stored on the chain for later tracing.

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