Privacy-preserving and Efficient Aggregation based on Blockchain for Power Grid Communications in Smart Communities

• Smart meters collect near real-time electricity data that utilities use for scheduling, dynamic pricing, and power services.Source: INTRODUCTION · p. 1
• Near real-time consumption data and power requests can reveal appliance-use patterns, user behavior, and periods when a home is unoccupied.Source: INTRODUCTION · p. 1
• Existing privacy-preserving approaches face challenges involving trusted aggregation, linking real identities to pseudonyms, and authentication overhead.Source: INTRODUCTION · p. 1
• The scheme uses randomly selected group users for aggregation and blockchain recording, multiple pseudonyms for identity unlinkability, and bloom filters with zero-knowledge proof for fast private authentication.Source: INTRODUCTION · p. 2
• Related techniques protect either user identity or user data, while blockchain is adopted here for decentralized data processing.Source: EXISTING PRIVACY-PRESERVING TECHNIQUES · p. 2, A. Protecting user’s identity · p. 2

• Household-battery schemes can hide consumption profiles, but privacy depends on battery capacity and may conflict with users’ economic interests.Source: B. Protecting user’s data · p. 2
• Blockchain links blocks through previous-block hashes and Merkle-tree roots, making backdated transaction changes difficult.Source: A. Blockchain · p. 2
• Proof-of-work imposes substantial computational cost because mining repeatedly tests random values until a valid block hash is found.Source: A. Blockchain · p. 3
• The scheme randomly selects the user whose data is closest to the time-slot average as mining node, avoiding proof-of-work for lightweight devices.Source: A. Blockchain · p. 3
• A bloom filter accelerates authentication by hashing an ID into array positions and checking whether the required positions contain zero.Source: B. Bloom Filter · p. 3
• A fabricated identifier can be detected when its bloom-filter mapping includes a zero position.Source: B. Bloom Filter · p. 3

• Zero-knowledge proof allows a verifier to prove input correctness without obtaining information about the input content.Source: C. Zero knowledge proof · p. 3
• The scheme treats a user’s public key as a pseudonym whose real identity is unknown to other parties.Source: C. Zero knowledge proof · p. 3
• The verifier decrypts the signed message and pseudonym with the public key to confirm pseudonym validity without learning the user’s real identity.Source: C. Zero knowledge proof · p. 3

• The network uses neighborhood area networks of smart meters and a wide area network carrying each group’s aggregated data to the center unit.Source: A. System model · p. 3
• Users are grouped by electricity-consumption type, and one user per time slot is selected to aggregate data and record it in a private blockchain.Source: A. System model · p. 3
• KMC generates multiple key pairs, treats public keys as pseudonyms, and distributes a group-specific bloom filter to users.Source: A. System model · p. 3
• The control center uses aggregated near real-time data to construct electricity profiles for power planning and dynamic pricing.Source: A. System model · p. 3
• The billing center calculates each user’s billing data from the blockchain of that user’s group.Source: A. System model · p. 3
• RSA initialization selects primes p and q, computes n = pq, and derives public and private key parameters for users.Source: B. System initialization · p. 4

• Each user replaces their true identity with pseudonyms and may bind consumption data to multiple pseudonyms for further obfuscation.Source: C. Data collection · p. 4
• In each time slot, users randomly split consumption data among different pseudonyms and publish the data, timestamp, pseudonym, and encrypted signature.Source: C. Data collection · p. 4
• The encrypted message concatenates consumption data, timestamp, and pseudonym, and is encrypted using the sender’s private key.Source: C. Data collection · p. 4
• Receivers verify pseudonym authenticity by checking whether the sender’s pseudonym correctly decrypts the signature.Source: D. Identity-authentication · p. 5
• A bloom filter checks pseudonym validity using k hash functions; a pseudonym is accepted when all mapped values are nonzero and otherwise dropped.Source: D. Identity-authentication · p. 5

• After identity authentication, users determine a mining node to aggregate group consumption data and record it on the blockchain.Source: E. Selection of mining node · p. 5
• Users first calculate the average consumption from received data, then select the user whose data is closest to that average.Source: E. Selection of mining node · p. 5
• Multiple equally close pseudonyms may all become mining nodes, but they generate the same new block.Source: E. Selection of mining node · p. 5
• Because no user knows the average before all group data are collected, a malicious user cannot easily increase the probability of selection.Source: E. Selection of mining node · p. 5

• The new block is published to group users, who verify its records and attach it to their local blockchain when correct.Source: F. Creation of new block · p. 6, G. verification of the block · p. 6
• The mining node hashes consumption data in a Merkle tree and records its root hash, timestamp, previous-block hash, pseudonym, and average in the block header.Source: F. Creation of new block · p. 6
• If no user disputes the block, the mining node sends the group’s summed consumption data to the control center.Source: G. verification of the block · p. 6
• Each user only needs to verify records related to their own data, and verification can occur offline for real-time dispatch.Source: G. verification of the block · p. 6
• The control center uses group sums to draw consumption profiles and offer dynamic pricing, while the billing center calculates bills from time-slot data recorded on the blockchain.Source: H. Dispatching and billing · p. 6
• Blockchain data integrity enables accurate billing from electricity consumption recorded across different time slots.Source: H. Dispatching and billing · p. 6

• An adversary outside the group learns only the summed consumption data, so individual private data are not disclosed.Source: A. Data Privacy · p. 6
• The mining-node selection limits an insider’s selection chance because the accurate average is unpredictable before all group data are collected.Source: A. Data Privacy · p. 6
• Blockchain records are verified by all group users, providing a check on blocks generated by the mining node.Source: A. Data Privacy · p. 6
• Asymmetric encryption and zero-knowledge proof protect pseudonym authenticity because forging a legal pseudonym requires the private key.Source: B. Authentication · p. 6
• The bloom filter quickly checks whether pseudonyms are registered, rejecting constructed pseudonyms whose mapping values contain a zero.Source: B. Authentication · p. 6
• Bloom-filter hash collisions create a nonzero error probability, which decreases as the array size increases and may then be ignored.Source: B. Authentication · p. 6

Authentication time complexity

• The bloom-filter scheme has much lower authentication time complexity than the conventional scheme without a bloom filter.The evaluation assumes a bloom-filter error probability of 0.01 and groups of up to 200 users.Source: PERFORMANCE EVALUATION · p. 6
• Figure 5 compares authentication with and without a bloom filter.Source: PERFORMANCE EVALUATION · p. 7

Computational cost

• The proposed scheme has less computational cost than PPM-HAD and DG-APED.Source: PERFORMANCE EVALUATION · p. 7
• Figure 6 compares the proposed scheme with PPM-HAD and DG-APED in computational cost.Source: PERFORMANCE EVALUATION · p. 7

CONCLUSION

• The scheme selects a mining node using differences between users’ consumption data and the average value to support fair selection.Source: CONCLUSION · p. 7
• The selected mining node records users’ data in a group blockchain, which is published within the group to ensure message integrity.Source: CONCLUSION · p. 7
• Pseudonyms and multiple pseudonyms protect user identities and provide further obfuscation against snooping by group members.Source: CONCLUSION · p. 7
• Bloom filters are adopted for fast authentication, and evaluation reports significant computational-efficiency advantages over conventional and other popular aggregation schemes.Source: CONCLUSION · p. 7
• Future work will reduce authentication-related computational overhead, especially during system initialization.Source: CONCLUSION · p. 7

Author biographies

• Zhitao Guan is an associate professor at North China Electric Power University whose research focuses on smart-grid, wireless, and cloud security.Source: BIOGRAPHIES · p. 7
• Guanlin Si is a master’s candidate at North China Electric Power University researching smart-grid security.Source: BIOGRAPHIES · p. 8
• Xiaosong Zhang is a professor at the University of Electronic Science and Technology of China researching network and information technology security and applications.Source: BIOGRAPHIES · p. 8
• Longfei Wu is an assistant professor at Fayetteville State University whose research includes security and privacy issues in mobile devices.Source: BIOGRAPHIES · p. 8
• Nadra Guizani lectures at Gonzaga University and studies disease-spread prediction, mobile networking, large-data analysis, and prediction techniques.Source: BIOGRAPHIES · p. 8
• Xiaojiang Du is a Temple University professor whose research interests include wireless networks, security, and systems.Source: BIOGRAPHIES · p. 8