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TrustChain: Trust Management in Blockchain and IoT supported Supply Chains
Sidra Malik, Volkan Dedeoglu, Salil S. Kanhere, Raja Jurdak
TL;DR
Supply-chain blockchains provide immutable traceability but cannot establish the authenticity of physical-event data, while existing reputation systems lack sufficient observation breadth, granularity, automation, and overhead analysis. TrustChain addresses these gaps through a three-layered consortium-blockchain framework with multi-source, product-specific reputation calculations automated by smart contracts, and its evaluation reports minimal throughput and latency overhead.
Problem
Blockchain preserves supply-chain records but cannot verify the authenticity of physical-event observations, while existing reputation systems lack multi-source evidence, granularity, automation, and overhead analysis.
Method
TrustChain uses a three-layered consortium blockchain to track participant interactions and smart contracts to calculate agent-, commodity-, and product-specific reputation scores from multiple observations.
Results
Evaluations report minimal throughput and latency overhead compared with blockchain trading without trust management, alongside high resilience against eight of nine considered attacks.
Takeaways & Limitations
TrustChain dynamically assigns reputation to both supply-chain participants and commodities while automating trust management through smart contracts.
Abstract
from arXiv · showhide
Traceability and integrity are major challenges for the increasingly complex supply chains of today's world. Although blockchain technology has the potential to address these challenges through providing a tamper-proof audit trail of supply chain events and data associated with a product life-cycle, it does not solve the trust problem associated with the data itself. Reputation systems are an effective approach to solve this trust problem. However, current reputation systems are not suited to the blockchain based supply chain applications as they are based on limited observations, they lack granularity and automation, and their overhead has not been explored. In this work, we propose TrustChain, as a three-layered trust management framework which uses a consortium blockchain to track interactions among supply chain participants and to dynamically assign trust and reputation scores based on these interactions. The novelty of TrustChain stems from: (a) the reputation model that evaluates the quality of commodities, and the trustworthiness of entities based on multiple observations of supply chain events, (b) its support for reputation scores that separate between a supply chain participant and products, enabling the assignment of product-specific reputations for the same participant, (c) the use of smart contracts for transparent, efficient, secure, and automated calculation of reputation scores, and (d) its minimal overhead in terms of latency and throughput when compared to a simple blockchain based supply chain model.
I. INTRODUCTION
Blockchain can improve supply-chain traceability, but it cannot establish whether physical-event data is authentic or whether entities and commodities are trustworthy. TrustChain addresses this gap with granular, multi-source reputation management automated through smart contracts, while reporting minimal performance overhead.
- Blockchain records supply-chain events immutably, but cannot verify the authenticity of observations supplied by entities about physical commodities.
- IoT sensors and other observations can support trust assessment, yet sensors may be faulty or attacked and existing systems lack product-specific granularity.
- TrustChain evaluates commodity quality and entity trustworthiness from multiple data sources at both agent and resource levels.
- Smart contracts automate reputation calculation, rewards, and penalties for supply-chain participants and traded commodities.
- Evaluations report minimal throughput and latency overhead compared with blockchain trading without trust management.
- Existing blockchain reputation approaches commonly use single observations, lack adversarial analysis, and do not quantify trust-model overhead.
III. REPUTATION AND TRUST IN BC BASED SUPPLY CHAINS
TrustChain combines supply-chain data, a permissioned blockchain, and an application layer to record authenticated event digests and apply controlled processing. Smart contracts provide automated execution within this layered architecture.
- TrustChain requires both tamper-proof recording and authentic observations for supply-chain events, product properties, sensor data, and endorsements.
- Smart contracts are self-executing programs invoked when predefined conditions are met.
- The framework has data, blockchain, and application layers supported by access-control components.
- Raw data may remain off-chain while its message digest is submitted as a blockchain transaction for scalable processing.
- TrustChain is implemented with a permissioned blockchain managed by a business network administrator, using Hyperledger Fabric for deployment.
- Static public keys identify entities, while validating peers check query and write transactions against ACL rules.
A. Data Layer
TrustChain’s data layer converts sensor streams, trade events, and regulatory endorsements into blockchain transactions. These inputs support ratings for commodities and entities, including mechanisms for handling disputed ratings.
- Sensor streams, trade events, and regulatory endorsements provide inputs from which the data layer generates blockchain transactions.
- IoT sensors monitor temperature, location, and humidity, with calibrated temperature readings used to indicate food quality against defined thresholds.
- Trade transactions record commodity ownership changes and append buyer ratings for sellers based on pre-negotiated quality assessments.
- An unfair rating can be challenged through a seller-issued dissatisfaction flag sent to a validator for recalculation.
- Regulatory ratings are generated from certificates and reports produced through food-safety inspections and HACCP checks.
- Smart contracts generate sensor, trader, and regulatory ratings that the blockchain trust module uses to calculate entity and commodity reputation values.
B. BC Layer
The BC layer governs supply-chain transactions and maintains profiles for entities and commodities under access-control rules. It records commodity creation, ownership transfers, sensor data, regulatory ratings, and receipt completion on the ledger.
- The access-control list defines transaction permissions, ledger access, profile updates, and other entity actions.
- Commodity creation binds a new commodity to a quality contract and records its identifier, hashed data, owner, and authentication information.The quality contract specifies assessment terms, temperature bounds, and rating criteria.
- Trade transactions record each physical commodity handover by replacing the current owner with the buyer and including seller and buyer signatures and public keys.
- Sensor transactions record hashed IoT temperature streams through gateway nodes, with generation decoupled from commodity-trade frequency.
- Regulator transactions record seller ratings from inspection evidence for each commodity type, while receipt transactions mark chain completion and update the commodity’s overall rating.Receipt records can help identify commodities without progressive chains as fake and make quality ratings visible to consumers.
2) Smart Contracts:
TrustChain uses quality and rating smart contracts to derive commodity and seller ratings from supply-chain events. The contracts automate transparent calculations using sensor, buyer, and regulator observations, with configurable weighting and commodity-specific conditions.
- Smart contracts calculate entity and commodity ratings when related supply-chain transactions are recorded on the blockchain.The calculations are described as transparent, secure, efficient, and automated, without intermediaries.
- Each commodity receives a quality contract containing its identifier, temperature thresholds, and quality-rating criteria.Thresholds distinguish boundary conditions for safety from damage conditions that trigger further responses.
- Temperature readings submitted through sensory transactions produce warning notifications at damage thresholds and a commodity reputation score based on temperature history.The score is updated at each trade event and becomes an overall commodity rating when the retailer records receipt.
- The rating contract computes seller reputation from commodity reputation, regulator rating, and buyer rating using configurable weights.Weights can reflect supply-chain conditions, such as assigning less weight to infrequent sensor data or outdated regulator ratings.
3) Reputation and Trust Module:
TrustChain aggregates reputation over supply-chain events with a time-varying, amnesic model that emphasizes recent observations. It stores separate reputations for different commodity types and initializes newcomers at a minimum trust score.
- TrustChain uses time-varying, amnesic trust management in which recent supply-chain events receive higher weights than older events.
- A new trader with no past reputation receives the initial trust score Trustmin.The trust calculation then combines current and previous reputation scores before incorporating application-specific features.
- The overall reputation score aggregates reputation values from current and previous supply-chain events using a decaying forgetting factor.The factor allows reputation to evolve over time while giving recent events greater influence.
- For traders handling multiple commodity types, reputation is calculated and stored separately for each type to provide granularity.Periodic recalculation is controlled by the business network administrator or consortium administrators.
4) Trust Evaluation:
TrustChain updates trader trust using reputation and application-specific features, then compares the result with a minimum participation threshold. Violations notify the administrator, who may revoke network participation.
- Trust combines overall reputation with weighted application-specific features such as sales volume, successful transactions, and consumer feedback.The weighting factors are determined by the business network administrator.
- Feature scores can be assigned using administrator-defined values for successful transactions and may also receive forgetting factors.
- When Ttrader(tn) < Trustmin, TrustChain generates a notification about the violation and the administrator can revoke the trader from the network.
C. Application Layer:
The application layer handles queries and transaction requests from administrators, regulators, and consumers, and applies penalties or rewards based on query results and issuing authority.
- The application layer addresses queries and transaction requests from administrators, regulators, and consumers.
- It implements penalties and rewards according to query results and the issuing authority’s role.
1) Queries:
TrustChain supports read/write and read-only queries, automates seller reputation updates through smart contracts, and enables reputation-based incentives and penalties.
- Queries: Read/write queries first read ledger data and then compute values such as a seller’s trader reputation.
- Queries: Each trade transaction automatically computes the seller’s product-specific reputation, while overall trader reputation is calculated on administrator request for a defined period.
- Queries: Read queries return ledger data such as sensor ratings for commodities and reputation or chain-completeness information about commodities and traders.
- Queries: Publishing entities with the highest trust values rewards honest participants by helping them find customers and increase sales.
- Queries: Low-trust entities can be penalized through temporary network revocation and publication of the revoked-participant list.
- The evaluation covers qualitative security and privacy analysis, a proof-of-concept implementation, and quantitative performance measurement.
A. Security and Privacy Analysis
TrustChain is analyzed against reputation-system attacks and evaluated through a Hyperledger proof of concept and Caliper-based performance tests.
- Security and Privacy Analysis: The threat model treats regulators, network administrators, and Hyperledger peers as honest, while considering individual or colluding supply chain entities as adversaries.
- Security and Privacy Analysis: TrustChain shows high resilience against eight of nine considered attacks and moderate resilience against the remaining attack.
- Security and Privacy Analysis: A dissatisfaction-flag safeguard reduces a buyer’s rating weight when multiple sellers flag the buyer and flagging remains below their trade count.
- Proof of Concept Implementation: The proof of concept models commodity trading among a primary producer, shipper, and retailer using Hyperledger Composer and REST APIs.
- Proof of Concept Implementation: The implementation creates quality contracts, registers entities with initial trust scores, records commodity creation and sensor transactions, and generates temperature alerts.
- Performance Evaluation: Caliper evaluates the Hyperledger model’s latency and throughput using a solo orderer, two endorsing peers, and one communication channel.
- Performance Evaluation: Tests vary TXcr and TXtr send rates from 10 to 100 transactions per second over 100 seconds, averaging 10 runs per rate.
D. Throughput Performance of TrustChain
TrustChain’s trade and create transactions exhibit throughput saturation, while the trust-management overhead remains small relative to the baseline blockchain system.
- D. Throughput Performance of TrustChain: Throughput measures the rate at which transactions are committed to the ledger after being issued by a trader.
- D. Throughput Performance of TrustChain: TrustChain trade throughput is only five transactions below the baseline at 70 tps, despite computing seller reputation and updating commodity ownership.
- D. Throughput Performance of TrustChain: Trade throughput rises linearly until approximately 40 tps, then decreases as validating-peer capacity reaches saturation.
- D. Throughput Performance of TrustChain: The baseline system shows the same declining-throughput trend, beginning around 40–50 tps.
- D. Throughput Performance of TrustChain: Create transactions throttle validators around 35–40 tps, slightly earlier than trade transactions because resource validation adds delay.
E. Latency Performance of TrustChain
TrustChain’s latency is measured from transaction submission to ledger commitment, with average latency evaluated for transaction types Txtr and Txcr. Latency rises sharply near throughput saturation, while TrustChain remains only slightly higher than the baseline.
- Latency measurement: Latency measures the time from application submission until a transaction is committed to the Hyperledger ledger.The cumulative latency comprises endorsement, broadcast, commit, and ordering latency.
- Latency measurement: Figures 6 and 7 report average latency for Txtr and Txcr, respectively.
- Comparison: TrustChain’s average latency is slightly higher than the baseline.
- Saturation behavior: Near the saturation point for maximum throughput, latency increases significantly because ordered transactions accumulate in the validation queue.The resulting congestion increases transaction committing latency.
- Saturation behavior: For Txcr, higher send rates throttle validation, increasing endorsement delays and slowing throughput as latency rises.Creating new commodities reaches minimum throughput when latency reaches its maximum.
- Operational implication: Validator selection may need to distribute transaction load according to the send rate in each supply-chain activity region.