# DPtracer: Integrating Log-Driven Accountability into Data Provision Networks
## 9 Related Work  
Advancements in cryptographic data structures or secure storage systems have significantly led to the development of secure logging mechanisms. The integration of robust hardware solutions, such as Trusted Execution Environments (TEEs), along with the incorporation of blockchain technology, has further enhanced the capabilities of secure logging systems.  
[PERSON] and [PERSON] [12] introduced History Tree with a set of semantics for tamper-evident logs, enabling efficient audits of an untrusted logger's stored events and the historical consistency of the log. With a versioned construction, History Tree supports membership, and incremental proofs enable clients to validate the log's outputs. Based on this structure, [PERSON] and [PERSON] [20] proposed Balloon, which combines two data structures (a hash tree and a History Tree) to create a forward-secure authenticated data structure. Balloon supports efficient membership and non-membership proofs and enables the log clients to verify the correctness of inserts without retaining a full copy of the data structure. We also further developed the History Tree into the Process Tree by incorporating the Merkle tree's principles with versioning of log entries. Furthermore, the Process Tree supports input-referencing and leaf-grouping, which enhance inspection of dispute resolution through data-flow reconstruction.  
Figure 12: Overhead in the DA-DPN and NA-DPN scenarios.  
Similar to our approach, the Merkle tree has been adapted to support tamper-evident logging across various environments. For large-scale log streams, [PERSON] et al. [23] enhanced the Merkle tree to a concurrent authenticated tree by adding chameleon hashing to enable concurrent log additions. More recently, [PERSON] and [PERSON] [24] proposed a tamper-evident logging system tailored for Internet of Things (IoT) devices that records all outputs from IoT devices to ensure node-level accountability. Our approach, however, focuses on providing tamper-evident logging for DPNs by enforcing mutual verification based on log records and integrating these verification steps directly into the protocol.  
Custos [17], proposed by [PERSON] et al., comprises a tamper-evident logging layer that integrates with existing frameworks. It uses TEE when adding a log entry, where each log entry is hashed and signed within the TEE enclave. Any modification of the log records can be detected during periodic audits. Similarly, in DPTracer, we use in-process verification to detect any log modification promptly and ensure its comprehensiveness.  
Incorporating blockchain as append-only data storage for logs has been actively researched. However, adding log entries directly to the blockchain is impractical because of the low transaction speeds and high costs associated with blockchain. Recent research has focused on using off-chain components to condense multiple log events into summarized data, resulting in fewer transactions. [PERSON] et al. [28] use a Merkle tree to aggregate raw sensing data to a digest, which is then written to the blockchain rather than storing the raw data directly. Similarly, in WedgeBlock [29], authored by [PERSON] et al., off-chain nodes function like a cache system for log data. It responds to the user's request while deferring the actual blockchain writing transaction to store the log. In DPTracer, the commitment can be compared with the condensed data offered by off-chain nodes in the context of blockchain. However, the primary goal of DPTracer is to ensure tamper-evident logging of the entire data provision process.