Cache data integrity verification method for deep space NDN (Named Data Networking) network

Abstract

The invention discloses a cache data integrity verification method oriented to a deep space NDN network, relates to the technical field of network communication and information security, and solves the problems that in the prior art, deep space NDN cache data is prone to damage due to radiation, retransmission delay is high, and an active verification mechanism suitable for on-satellite resource-limited nodes is lacked. According to the method, four entities including a producer, a consumer, a content storage cache module and an auditor complete verification through an initialization stage, a data uploading stage, an auditing tree construction stage, an auditing stage and damage processing. According to the method, the integrity and authenticity of the cached data and signature of the NDN node under the influence of a large number of cosmic rays in the deep space are efficiently verified and ensured, so that remote data retransmission caused by data damage is reduced to the maximum extent, the overall end-to-end transmission delay is remarkably reduced, and the real-time performance and efficiency of deep space communication are improved.

Description

Technical Field

[0001] This invention relates to the field of network communication and information security technology, specifically to a method for verifying the integrity of cached data for the Named Data Network (NDN) in Deep Space. Background Technology

[0002] As human exploration of deep space continues, reliable and efficient deep space communication systems have become critical infrastructure. Traditional connection-based network architectures (such as TCP / IP) struggle to meet the performance requirements when faced with the inherent challenges of long-distance, high-latency, and unstable links in deep space transmission.

[0003] Named Data Networking (NDN), as a content-centric network architecture, is considered a potential solution for revolutionizing deep space network communication due to its features such as content naming and distributed caching. NDN's caching mechanism allows data to be stored at nodes along the path (such as relay satellites), effectively reducing end-to-end latency in data retrieval.

[0004] However, applying NDN to the extreme environment of deep space introduces new challenges. Cache nodes in deep space (such as orbiting satellites) are exposed to strong cosmic ray radiation for extended periods, which may lead to single-event upsets (SEUs), causing bit flips in the data stored in the cache and threatening data integrity.

[0005] Although NDN design includes a producer-signed content mechanism, this verification occurs at the consumer end. If the consumer discovers an error in the content or a corrupted signature at the packet receiver, causing verification to fail, the consumer must resend the interest packet. In the context of deep space communication, if this data must be retrieved from a distant original producer (such as a lunar probe), it will inevitably introduce catastrophic end-to-end latency, directly negating the advantages of NDN's caching mechanism.

[0006] Furthermore, deep space nodes (such as satellites) generally face limitations in computing power and storage capacity. Therefore, there is an urgent need for an efficient and low-overhead data integrity verification mechanism. This mechanism needs to be deployable on resource-constrained on-board nodes to proactively verify the integrity of NDN cached data and its signature before data is requested by consumers, thus avoiding the high retransmission overhead caused by data corruption. Summary of the Invention

[0007] To address the problems in existing technologies, such as the high retransmission delay caused by radiation damage to cached data in deep space NDN networks, and the lack of an active verification mechanism suitable for resource-constrained onboard nodes, this invention provides a method for verifying the integrity of cached data in deep space NDN networks.

[0008] Step S1. The producer generates a key pair for data signing and a key pair for generating audit tags;

[0009] Step S2. The Producer divides the original data into multiple data blocks and calculates a data signature for each data block. and audit labels Then encapsulated into data packets Transmitted through the NDN network and cached by NDN CS along the way;

[0010] Step S3. Audit tree construction;

[0011] The auditor listens for cached events in the NDN CS when a data packet... When cached, the Auditor extracts the content name. and audit labels This is then inserted into the locally maintained audit tree CSNAT, which is named based on content storage.

[0012] Step S4. Auditing Phase: This phase is executed periodically; the Auditor uses batch verification to perform probabilistic checks on the integrity of cached data.

[0013] Step S41. The Auditor initiates an integrity challenge to the NDN CS and randomly selects a content name prefix based on CSNAT to perform integrity verification on the cached data packets under the selected prefix;

[0014] Step S42. NDN CS calculates the corresponding cached data integrity evidence based on the challenge request and returns the storage proof to the Auditor;

[0015] Step S43. The Auditor calculates the storage proof returned by the NDN CS and verifies whether it matches the prefix in CSNAT. Finally, it determines whether the data and its signature are complete.

[0016] The beneficial effects of this invention are:

[0017] 1. Significantly Reduced End-to-End Latency: This invention ensures the integrity of data cached at deep-space nodes (such as GEO satellites) through proactive auditing by the Auditor and an "early retransmission" mechanism. When a consumer requests data, it has a high probability of hitting a valid copy in a nearby cache, without needing to retransmit from a distant producer (such as the Moon) due to data corruption. Earth-Moon communication simulation experiments show that, as Figure 4 This invention can reduce the average end-to-end latency by approximately 83.6% (from approximately 1587ms to approximately 370ms).

[0018] 2. Efficient Audit Tree Structure (CSNAT): The CSNAT audit tree designed in this invention has a structure that naturally aligns with the NDN naming prefix system. Compared to the traditional Merkle Hash Tree (MHT), CSNAT utilizes a multi-branch naming tree design, resulting in a shallower tree depth and a smaller average number of nodes required to be updated when inserting or modifying data (experimental data shows a reduction of nearly 50%). This significantly reduces the maintenance overhead of the audit structure, such as... Figure 5 This improves verification efficiency.

[0019] 3. Low computational and storage overhead: This scheme is suitable for the limited computing power and storage resources of deep space nodes. CSNAT has low storage overhead. Furthermore, this scheme significantly reduces computational complexity by optimizing the verification algorithm (e.g., optimizing multiple pairing calculations into a single aggregation pairing operation). Experiments show that, as... Figure 6 By optimizing multiple public key pairing operations into a single aggregate pairing operation, this invention reduces the computation time for verifying 1000MB of data by approximately 48% compared to traditional verification methods.

[0020] 4. Reduce unnecessary data retransmissions: The method of this invention employs "dual signatures" (data signature). +Audit Tag This invention employs a two-step verification mechanism. Compared to traditional single-signature schemes that cannot distinguish the source of errors, this invention's method uses two steps (first verifying the aggregate tag, then verifying the ECDSA signature) to distinguish between data content corruption and signature corruption only, avoiding unnecessary retransmission of the complete content and effectively reducing the amount of retransmitted data in the high-communication-cost deep space network.

[0021] 5. Security Assurance: The security of the method in this invention is based on standard cryptographic assumptions (such as EUF-CMA security of ECDSA and the discrete logarithm problem). Audit Tag and signature None of them can be forged. Meanwhile, the random numbers used in each round during the audit phase... This ensures the method is effective against replay attacks. CSNAT's aggregation feature guarantees that any data corruption can be traced and located. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of a deep space NDN network cache verification model, which is part of the cache data integrity verification method for deep space NDN networks described in this invention.

[0023] Figure 2 This is a schematic diagram of the audit tree construction in this invention;

[0024] Figure 3 This is a schematic diagram illustrating the integrity verification of the present invention;

[0025] Figure 4 This is a diagram illustrating the trend of end-to-end transmission delay over time.

[0026] Figure 5 A comparison chart showing the average number of update nodes for different audit trees as cache size increases;

[0027] Figure 6 This diagram illustrates the comparison of verification time overhead under different data block sizes. Detailed Implementation

[0028] Specific Implementation Method 1: Combination Figures 1 to 3 This embodiment describes a method for verifying the integrity of cached data in deep space NDN networks. This method efficiently verifies and ensures the integrity and authenticity of cached data and signatures of NDN nodes under the influence of numerous cosmic rays in deep space, thereby minimizing remote data retransmissions caused by data corruption, significantly reducing overall end-to-end transmission latency, and improving the real-time performance and efficiency of deep space communication.

[0029] The verification method described in this embodiment includes four core entities:

[0030] Producer: The owner of the raw data. Responsible for generating data packets, which, in addition to the data content, also include signatures and audit tags for verification. The producer responds to packets of interest in the network and sends data packets.

[0031] Consumer: The requester of data. Sends interest packets to the NDN network to request data and receives data packets.

[0032] Content Storage Caching Module (NDN CS): This caching module is deployed on deep space network nodes (such as GEO satellites). Internally, this module maintains a CSNT (Content Store Name Tree) for efficient management and retrieval of locally cached data packets based on a hierarchical naming index. The module is responsible for caching data packets flowing through the nodes and providing storage proofs in response to integrity challenges initiated by auditors.

[0033] Auditor: A logical entity responsible for performing integrity verification, which can be deployed on cache nodes. It is responsible for building an audit tree (CSNAT) to manage the verification labels of cached data and periodically challenges the NDN CS to verify the proofs returned by the NDN CS to ensure the integrity of cached data and signatures.

[0034] The verification method described in this embodiment includes five stages: initialization stage, data upload stage, audit tree construction stage, audit stage, and corruption handling; as detailed below:

[0035] Step S1. Initialization phase: The Producer runs the key generation algorithm (KeyGen) to generate key pairs for data signing (such as ECDSA keys) and key pairs for generating audit tags (such as keys based on bilinear mappings).

[0036] like Figure 1 As shown, in this embodiment, during the initialization phase of step S1, the process of generating a key pair using the key generator (KeyGen) is as follows:

[0037] Generate security parameters: This algorithm takes security parameters as input. Output common parameters According to safety parameters Determine the group for the bilinear mapping and their generators And define a bilinear mapping. Then determine a series of hash functions, including ( (It is the order of the linear group). , Output common parameters ={ };

[0038] Key Pair Generation: This algorithm, run by the Producer, generates key pairs for data signing and integrity verification. The algorithm uses public parameters... As input, generate a private key. and public key The private key consists of two parts: ,in, It is the audit private key used to calculate the tag. This is the private key used for ECDSA signing. The corresponding public key is... in = It is the audit public key used to calculate the tag. It is the public key for ECDSA signing.

[0039] Step S2. Data Upload Stage: The Producer runs the data generation algorithm (DataGen) to divide the data into chunks. , It is a cyclic group of integers. The order of the cyclic group is given; and a data signature is calculated for each block of data. (such as ECDSA signatures) and audit labels The producer encapsulates it into a data packet. Data packets are transmitted through the NDN network and buffered by NDN CSs along the way; among them, This includes additional information about the NDN data packet, such as timestamp, freshness, priority, etc.

[0040] In this implementation, when the Producer detects that the Consumer is sending interest packets to the NDN network, it needs to format and encapsulate the data and generate a corresponding double-signature structure to ensure the integrity and tamper resistance of the data during storage and transmission in the deep space environment. Subsequently, the data packets are cached in the NDN CS through the NDN network and finally received by the Auditor to build an audit tree.

[0041] Generate data packet: Receive private key Content Name ( ) and common parameters Output the data packet corresponding to the content name. .

[0042] First, the producer divides the raw data M into multiple data blocks F: For each data block Assign a unique content name ;

[0043] ;

[0044] Secondly, the Producer uses the private key signed by ECDSA. Calculate each data block Data signature ,in This signature ensures the integrity of the data packet content and the authenticity of its origin, preventing unauthorized tampering or forgery.

[0045] Then, the Producer bases its data on each block. Calculate audit labels ,in:

[0046]

[0047] The audit tag is used to cache the integrity verification of NDN CS to improve verification efficiency.

[0048] Finally, the audit label With data blocks and its signature Encapsulated together in the NDN data packet:

[0049]

[0050] in, This includes additional information about the NDN data packet, such as timestamp, freshness, priority, etc.

[0051] The Producer publishes data packets over the NDN network. Each data packet is indexed by its content name and cached in the NDN CS.

[0052] Step S3. Audit Tree Construction: The Auditor listens for cached events in the NDN CS, and when a data packet... When cached, the Auditor extracts its content name as and audit labels This is then inserted into the locally maintained Content Storage Named Audit Tree (CSNAT).

[0053] In this embodiment, CSNAT is a multi-way tree whose structure is based on the naming prefix of NDN, and the value of the parent node is the aggregation of the label values ​​of all its child nodes.

[0054] In step S3 of this embodiment, in the NDN deep space network environment, a large number of cosmic rays may induce single-event effects (SEE), causing bit flips or corruption in the cached data. Therefore, it is necessary to perform regular integrity verification on the NDN cached data. To efficiently manage and index the integrity information of the cached data, the auditor uses a Content Store Name Audit Tree (CSNAT) to store and update data tags. This structure can dynamically build a hierarchical index when data is cached to the NDN CS, enabling rapid location and verification of data integrity.

[0055] Generate audit tree: When NDN CS caches new packets:

[0056] At that time, the auditor listens for and retrieves cached events. and Then insert it into CSNAT and update the value of the parent node level by level.

[0057] First, the Auditor inserts a new data node into the CSNAT structure. ,in, It serves as an index to the tree and is stored within the multi-branch tree structure. As an integrity tag, it is bound to the corresponding content name to support integrity verification.

[0058] Then, update the value of the parent node level by level, setting... express In the CSNAT structure, the parent node's value is updated as follows:

[0059]

[0060] The update process is calculated layer by layer along the parent nodes until the root of the tree is updated. ,like Figure 2 As shown.

[0061] Step S4. Auditing Phase: This phase is executed periodically. The Auditor uses batch verification to perform probabilistic checks on the integrity of cached data; for example... Figure 3 As shown. Includes generating challenges ( ), Generate storage proof ( ) and Verify; the specific process is as follows:

[0062] Step S41. The Auditor initiates an integrity challenge to the NDN CS and randomly selects a content name prefix based on CSNAT, then performs integrity verification on the cached data packets under the selected prefix; specifically:

[0063] Generate Challenge The Auditor randomly generates a set of challenges. It randomly selects c naming prefixes ( ), and for each prefix Generate random numbers Generate a set of challenges : , …, };

[0064] Then, the Auditor sends the challenge set Q to the NDN CS.

[0065] Step S42. NDN CS calculates the corresponding cached data integrity evidence based on the challenge request and returns the storage proof to the Auditor; specifically:

[0066] Generate storage proof When the NDN CS receives an Auditor challenge request, it needs to calculate the storage proof. And return it to the auditor. NDN CS is based on each of the challenge sets. Locating cached content Then construct the storage proof. That is: NDN CS received a set of challenges. Then, based on the challenge's prefix, the corresponding data block is retrieved from the cache. Public key Information such as storage proof is generated. Return to the Auditor;

[0067] .

[0068] Step S43. The Auditor calculates the storage proof returned by the NDN CS, verifies whether it matches the prefix in CSNAT, and finally determines whether the data and its signature are complete; specifically:

[0069] Verification: The Auditor receives proof of storage. On the one hand, the Auditor, based on the challenge set Calculate the aggregated audit label in local CSNAT. On the other hand, the Auditor uses stored proofs. Data blocks returned from Calculate an expected value using information such as [list of factors]. The Auditor verifies this using a bilinear mapping. Check if it matches the expected value. If it does, the verification is successful, indicating that the cached data and signature are not corrupted. Output the verification result as 1 and end.

[0070] Auditor's verification equation:

[0071] ;

[0072] If the verification is successful, the data and signature are complete, and output 1; otherwise, the verification fails, and proceed to step S5.

[0073] Step S5. Damage Handling Stage:

[0074] If the verification in step S43 fails, it indicates that the cached data is corrupted. The Auditor performs recursive verification on CSNAT to further locate the data packet that failed the integrity verification. If the second verification fails, it is confirmed that the data (content or signature) is corrupted. The Auditor immediately triggers the retransmission of the data packet (i.e., "early retransmission") to repair the copy in the cache.

[0075] The specific verification is as follows:

[0076] Recursive execution algorithm Verify the prefix of the challenge in the audit tree. Child nodes And the integrity of the corresponding data and signature, until the data packet that failed verification is accurately located. ;

[0077] Further use of Auditor Data signature and public key Perform a second verification If the verification is successful, the cached data is considered to be intact and output 1. If the verification fails, the cached content is considered to be corrupted and output 0, and the producer is asked to retransmit the data packet.

[0078] Specific Implementation Method Two: Combination Figures 4 to 6 This embodiment describes an application example of the cached data integrity verification method for deep space NDN networks described in Specific Embodiment 1:

[0079] The verification method was applied to the high-latency and high-radiation environment of deep space, demonstrating its performance advantages and robustness. For example... Figure 4 As shown, the experiment simulated a lunar-Earth communication scenario (lunar-GEO link one-way delay of approximately 1284ms, GEO-Ground link one-way delay of approximately 142ms), comparing the evolution trend of end-to-end transmission delay over time. In the control group without an auditing mechanism, as radiation-induced cache data corruption accumulated, the hit rate of interest packets dropped sharply, forcing the system to frequently request retransmissions from the remote lunar producer, causing the average delay to surge to approximately 1587ms in the 10th hour, essentially losing its cache efficiency. In contrast, the proactive auditing mechanism introduced in this invention can periodically detect and "retransmit in advance" to repair corrupted data, maintaining the average end-to-end delay stably within a low range of 360ms to 500ms throughout the entire 10-hour simulation period, achieving an order-of-magnitude optimization of latency performance. Figure 5 As shown, the CSNAT structure proposed in this invention significantly outperforms the traditional Merkle tree in terms of dynamic maintenance overhead. Because the Merkle tree uses a binary hash structure, its update path depth increases logarithmically with the cache size. In contrast, CSNAT employs a multi-way tree and prefix index design that naturally aligns with the NDN namespace, significantly compressing the tree depth and update path. Experimental data shows that when the cache size reaches 8000 data packets, the average number of update nodes for CSNAT is only 6.5, compared to more than 13 nodes for Merkle trees, reducing maintenance overhead by approximately 50%. This demonstrates that this structure has higher update efficiency and scalability on resource-constrained satellite nodes. Figure 6 As shown, regarding verification time overhead, this invention employs deep algorithmic optimization to address the limited computing power of deep space nodes. By refactoring the cumbersome multiple bilinear pairing operations in traditional schemes into a single aggregation pairing operation and transferring group operations to the scalar domain, computational complexity is significantly reduced. Experiments demonstrate that while verification time increases linearly from 100MB to 1000MB, this invention maintains a significant advantage. Particularly in the 1000MB data block scenario, verification time is reduced by approximately 48% compared to traditional schemes, effectively alleviating the computational load on satellite platforms and improving the feasibility of engineering implementation.

[0080] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0081] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for verifying the integrity of cached data in deep space NDN networks, characterized by: This method is implemented by the following steps: Step S1. The producer generates a key pair for data signing and a key pair for generating audit tags; Step S2. The Producer divides the original data into multiple data blocks and calculates a data signature for each data block. and audit labels Then encapsulated into data packets Transmitted through the NDN network and cached by NDN CS along the way; Step S3. Audit tree construction; The auditor listens for cached events in the NDN CS when a data packet... When cached, the Auditor extracts the content name. and audit labels This is then inserted into the locally maintained audit tree CSNAT, which is named based on content storage. Step S4. Audit Phase: This phase is performed periodically; Auditor uses batch verification to perform probabilistic checks on the integrity of cached data; Step S41. The Auditor initiates an integrity challenge to the NDN CS and randomly selects a content name prefix based on CSNAT to perform integrity verification on the cached data packets under the selected prefix; Step S42. NDN CS calculates the corresponding cached data integrity evidence based on the challenge request and returns the storage proof to the Auditor; Step S43. The Auditor calculates the storage proof returned by the NDN CS and verifies whether it matches the prefix in CSNAT. Finally, it determines whether the data and its signature are complete.

2. The method for verifying the integrity of cached data in deep space NDN networks according to claim 1, characterized in that: It also includes step S5. If the verification fails in the auditing phase of step S4, it indicates that the cached data is corrupted; the Auditor performs recursive verification on CSNAT to further locate the data packet that failed the integrity verification. If the verification fails again, it is confirmed that the data is corrupted, and the Auditor immediately triggers the retransmission of the data packet.

3. The method for verifying the integrity of cached data in deep space NDN networks according to claim 2, characterized in that: In step S1, the Producer runs a key generation algorithm, and the process of generating key pairs through the key generator is as follows: With common parameters As input, generate a private key. and public key The private key consists of two parts: , It is the audit private key used to calculate the tag. This is the private key used for ECDSA signing; the corresponding public key is... It is the audit public key used to calculate the tag. It is the public key for ECDSA signing. It is a generator.

4. The method for verifying the integrity of cached data in deep space NDN networks according to claim 3, characterized in that: The specific process of step S2 is as follows: First, the Producer divides the original data M into multiple data blocks. For each data block Assign a unique content name ; Secondly, the producer uses a private key signed with ECDSA. Calculate each data block Data signature ,in, ; Then, the Producer bases its data on each block. Calculate audit labels ,in: ； In the formula, This is the audit private key used to calculate the tag. For hash functions; For generators; Finally, the audit label With data blocks and its signature They are encapsulated together into NDN data packets and distributed through the NDN network. Each data packet is indexed according to its content name and cached in the NDN CS. ； In the formula, This includes additional information about the NDN data packet.

5. The method for verifying the integrity of cached data in deep space NDN networks according to claim 4, characterized in that: In step S3, an audit tree is generated. When the NDN CS caches a new data packet, the auditor listens for the cache event and extracts the content name. and audit labels Then, it is inserted into CSNAT and the values ​​of the parent nodes are updated level by level until the root node is updated. .

6. The method for verifying the integrity of cached data in deep space NDN networks according to claim 5, characterized in that: In step S41, challenges are generated: The Auditor randomly generates a set of challenges, randomly selects c naming prefixes, and assigns a challenge to each prefix. Generate random numbers Generate a set of challenges And send the challenge set Q to NDN CS; 。 7. The method for verifying the integrity of cached data in deep space NDN networks according to claim 6, characterized in that: In step S42, storage proof is generated: after the NDN CS receives the Auditor challenge request, it extracts the corresponding data block from the cache based on the challenge prefix. Public key Information, generate storage proof Return to the Auditor; 。 8. The method for verifying the integrity of cached data in deep space NDN networks according to claim 7, characterized in that: In step S43, verification: The Auditor receives the storage proof. The Auditor, based on the challenge set Calculate the aggregated audit label in local CSNAT. ; And the Auditor uses stored proofs Data blocks returned from The information is used to calculate the expected value, and the Auditor verifies this using a bilinear mapping. Check if it matches the expected value; if it matches, the verification is successful; otherwise, the verification fails, and proceed to step S5.

9. A method for verifying the integrity of cached data in deep space NDN networks according to claim 8, characterized in that: In step S5, the Auditor performs recursive verification on CSNAT to locate the specific corrupted data packet. Further use of the Auditor Data signature and public key Perform a second verification If the verification is successful, the cached data is considered to be intact and output 1. If the verification fails, the cached content is considered to be corrupted and output 0, and the Producer is asked to retransmit the data packet.

10. A method for verifying the integrity of cached data in deep space NDN networks according to any one of claims 1-9, characterized in that: This method is implemented by the following entities: Producer: The owner of the raw data; responsible for generating data packets, which include not only the data content but also signatures and audit tags for verification. Producers respond to packets of interest in the network and send data packets. Consumer: The requester of data; sends interest packets to the NDN network to request data and receives data packets; Content storage and caching module NDN CS: A caching module deployed on Deep Space Network nodes; It is responsible for caching data packets flowing through the nodes and responding to integrity challenges initiated by auditors, providing proof of storage; Auditor: A logical entity responsible for performing integrity verification, which can be deployed on cache nodes; Responsible for building an audit tree to manage the verification labels of cached data, and periodically challenging the NDN CS to verify the storage proofs returned by the NDN CS, ensuring the integrity of cached data and signatures.

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