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Fine-Grained, Secure and Efficient Data
Provenance on Blockchain Systems
Pingcheng RUAN, Gang CHEN, Tien Tuan Anh DINH,
Qian LIN, Beng Chin OOI, Meihui ZHANG
Blockchain Is a Class of Database
Blockchain
Distributed
Database
Database
Bitcoin
Distributed
Transactional
Systems
Blockchain Basics
• P2P network
– Asynchronous transaction
• Byzantine environment
– Mutual distrusting setup
• Distributed ledger
– Smart contract
• Inherent provenance-preserving
– ONLY for offline analytical query
Contract Txn 1 Txn 2
Token.Transfer( A, B, 10)
Token.Transfer( C, D, 20)Token.Transfer( A, B, 20)
Motivation
Expose provenance information
to smart contracts both
Efficiently Securely
Enabler for provenance-
dependent smart contracts
Enrich the transaction semantics
Provenance-dependent Contracts
• Previous transfer precondition:
– Enough balance from the sender
– CURRENT STATE ONLY
• New transfer precondition:
– Historical balance > threshold
– Recipient not transacted with certain blacklisted addresses recently
– HISTORICAL STATE & PROVENANCE INFO
Workarounds
Workaround 1:
•Dump every thing into current
state
•Effort-needed, expensive,
error-prune
Workaround 2:
• Offline analytics + Online
transactions
• Break of serializability
• Transaction-ordering attacks
Workaround 3:
• Minimum system
instrumentation
• NOT protocol level (e.g.,
Hyperledger Fabric v1.0+)
• Data tampering
Holistic Approach:
• Protocol-level enhancement
 Secure
• Performance-aware
 Efficient
Account1_v1: 10
Account1_v2: 20
Account1_v3: 15
Account2_v2: 12
Challenges
• With clearly-defined transformation semantics
• E.g
• Map and reduce in Hadoop
• Select, join and aggregation in SQL
NO standardized operations
• Tamper evidence
• Integrity proof
Byzantine environment
• Gas mechanism
• Verifier’s dilemma
Ever-growing ledger
Block Structure
Block Header
Prev Hash hash Txn Digest
State Digest PoW Nonce
Txn List
Enhancement Basis (Merkle Tree Variants)
• Limitation
– Latest State only
• Tamper evidence
– Succinct digest (root hash)
– Integrity proof (access path)
Block Header
Account Address and Assoicated
Balance in Global State:
0xABC: 10 0xABCD: 15
0xABCE: 20 0xBC: 25
Previous Block Hash
Transaction MPT Root Hash
Nonce
Receipt MPT Root Hash
State MPT Root Hash = H(Z)
nilA: H(X) B: H(Y)Z
BC: H(V) C: 25X Y
10D: 15 E: 20V
<Updated Chaincode ID>_<Key>:
ccid1_k1 ccid2_k1
ccid3_k1 ccid3_k2
Block Header
State Root Hash
Previous Block Hash
Transaction Root Hash
G
E F
A B C
ccid1_k1
ccid2_k1
ccid3_k1
ccid3_k2
D
Bucket List
(a) (b)
Merkle Patricia Trie Merkle Bucket Tree
LineageChain Overview
Application Layer
• Provenance specification
– User-defined input-output dependency
• Provenance query handler
– Hist(stateID, [blockNum])
 (val, blkStart, txnID)
– Backward(stateID, blkNum)
 List<(depStateID, depBlkNum)>
– Forward(stateID, blkNum)
 List<(depStateID, depBlkNum)>
InputID1
InputID2
OutputID1
OutputID2
OutputID3
Backward
Dependency
Forward
Dependency
Application Layer
Recipient -> Sender
Execution Layer
• Receive
– Contract invocation context
– Provenance specification
• Compute
– Transaction results
– Concrete dependency
• Prepare Merkle DAG
– Introduce one layer of direction
– Hash reference to encode
provenance backward
dependency
Execution Layer
• Forward tracking
– Problem: Undecided forward dependency during state update
• Solution
– Lazily store forward dependency on the successor state entry
Storage Layer
• Problem
– Efficient version-based (historical) query for a state ID
• Solution:
– Deterministic Append-only Skip List
– Hash-based reference
After appending
versions 12 and 16
Evaluation
• MICRO benchmarking (vs. flat storage)
– Preference to recent version query (with DASL)
– More efficient BFS enabled by backtrack (with ForkBase)
• MACRO benchmarking (applied to Hyperledger Fabric v0.6 and
v1.3)
– Negligible runtime overhead
o Tiny proportion of latency
– Negligible storage overhead
o >70% of space for blocks
o 25% for historical states
o 2~4% for DASL indexes and hash pointers
Performance of Provenance Query
• vs. Workaround 2
– Compute data provenance offline and conditionally trigger online transaction
Micro Performance of Provenance Query
• vs. Workaround 1
– Dump everything into the current state
• vs. Workaround 3
– Use Hyperledger Fabric’s built-in HistoryDB
Runtime Overhead
• Transaction processing
Hyperledger Fabric v0.6 Hyperledger Fabric v1.3
Storage Overhead
Conclusion
• LineageChain
– Enabler for provenance-dependent blockchain applications
– Protocol-level enhancement w.r.t. efficiency and security
– Negligible performance and storage overhead
• Key designs
– User-defined dependency specification
– Merkle DAG with dependency tracking
– DASL index to accelerate data provenance query
– Adoption in Hyperledger Fabric (v0.6 & v1.3)
ThankYou!

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Fine-Grained, Secure and Efficient Data Provenance on Blockchain Systems

  • 1. Fine-Grained, Secure and Efficient Data Provenance on Blockchain Systems Pingcheng RUAN, Gang CHEN, Tien Tuan Anh DINH, Qian LIN, Beng Chin OOI, Meihui ZHANG
  • 2. Blockchain Is a Class of Database Blockchain Distributed Database Database Bitcoin Distributed Transactional Systems
  • 3. Blockchain Basics • P2P network – Asynchronous transaction • Byzantine environment – Mutual distrusting setup • Distributed ledger – Smart contract • Inherent provenance-preserving – ONLY for offline analytical query Contract Txn 1 Txn 2 Token.Transfer( A, B, 10) Token.Transfer( C, D, 20)Token.Transfer( A, B, 20)
  • 4. Motivation Expose provenance information to smart contracts both Efficiently Securely Enabler for provenance- dependent smart contracts Enrich the transaction semantics
  • 5. Provenance-dependent Contracts • Previous transfer precondition: – Enough balance from the sender – CURRENT STATE ONLY • New transfer precondition: – Historical balance > threshold – Recipient not transacted with certain blacklisted addresses recently – HISTORICAL STATE & PROVENANCE INFO
  • 6. Workarounds Workaround 1: •Dump every thing into current state •Effort-needed, expensive, error-prune Workaround 2: • Offline analytics + Online transactions • Break of serializability • Transaction-ordering attacks Workaround 3: • Minimum system instrumentation • NOT protocol level (e.g., Hyperledger Fabric v1.0+) • Data tampering Holistic Approach: • Protocol-level enhancement  Secure • Performance-aware  Efficient Account1_v1: 10 Account1_v2: 20 Account1_v3: 15 Account2_v2: 12
  • 7. Challenges • With clearly-defined transformation semantics • E.g • Map and reduce in Hadoop • Select, join and aggregation in SQL NO standardized operations • Tamper evidence • Integrity proof Byzantine environment • Gas mechanism • Verifier’s dilemma Ever-growing ledger
  • 8. Block Structure Block Header Prev Hash hash Txn Digest State Digest PoW Nonce Txn List
  • 9. Enhancement Basis (Merkle Tree Variants) • Limitation – Latest State only • Tamper evidence – Succinct digest (root hash) – Integrity proof (access path) Block Header Account Address and Assoicated Balance in Global State: 0xABC: 10 0xABCD: 15 0xABCE: 20 0xBC: 25 Previous Block Hash Transaction MPT Root Hash Nonce Receipt MPT Root Hash State MPT Root Hash = H(Z) nilA: H(X) B: H(Y)Z BC: H(V) C: 25X Y 10D: 15 E: 20V <Updated Chaincode ID>_<Key>: ccid1_k1 ccid2_k1 ccid3_k1 ccid3_k2 Block Header State Root Hash Previous Block Hash Transaction Root Hash G E F A B C ccid1_k1 ccid2_k1 ccid3_k1 ccid3_k2 D Bucket List (a) (b) Merkle Patricia Trie Merkle Bucket Tree
  • 11. Application Layer • Provenance specification – User-defined input-output dependency • Provenance query handler – Hist(stateID, [blockNum])  (val, blkStart, txnID) – Backward(stateID, blkNum)  List<(depStateID, depBlkNum)> – Forward(stateID, blkNum)  List<(depStateID, depBlkNum)> InputID1 InputID2 OutputID1 OutputID2 OutputID3 Backward Dependency Forward Dependency
  • 13. Execution Layer • Receive – Contract invocation context – Provenance specification • Compute – Transaction results – Concrete dependency • Prepare Merkle DAG – Introduce one layer of direction – Hash reference to encode provenance backward dependency
  • 14. Execution Layer • Forward tracking – Problem: Undecided forward dependency during state update • Solution – Lazily store forward dependency on the successor state entry
  • 15. Storage Layer • Problem – Efficient version-based (historical) query for a state ID • Solution: – Deterministic Append-only Skip List – Hash-based reference After appending versions 12 and 16
  • 16. Evaluation • MICRO benchmarking (vs. flat storage) – Preference to recent version query (with DASL) – More efficient BFS enabled by backtrack (with ForkBase) • MACRO benchmarking (applied to Hyperledger Fabric v0.6 and v1.3) – Negligible runtime overhead o Tiny proportion of latency – Negligible storage overhead o >70% of space for blocks o 25% for historical states o 2~4% for DASL indexes and hash pointers
  • 17. Performance of Provenance Query • vs. Workaround 2 – Compute data provenance offline and conditionally trigger online transaction
  • 18. Micro Performance of Provenance Query • vs. Workaround 1 – Dump everything into the current state • vs. Workaround 3 – Use Hyperledger Fabric’s built-in HistoryDB
  • 19. Runtime Overhead • Transaction processing Hyperledger Fabric v0.6 Hyperledger Fabric v1.3
  • 21. Conclusion • LineageChain – Enabler for provenance-dependent blockchain applications – Protocol-level enhancement w.r.t. efficiency and security – Negligible performance and storage overhead • Key designs – User-defined dependency specification – Merkle DAG with dependency tracking – DASL index to accelerate data provenance query – Adoption in Hyperledger Fabric (v0.6 & v1.3)