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Privacy-preserving and Efficient Aggregation based on Blockchain for Power Grid Communications in Smart Communities
Zhitao Guan, Guanlin Si, Xiaosong Zhang, Longfei Wu, Nadra Guizani, Xiaojiang Du, Yinglong Ma
TL;DR
Near real-time smart-meter data supports smart-grid scheduling but can expose users’ behaviors, activity plans, and identities, while aggregation faces trust, pseudonym-linkage, and authentication-speed challenges. The paper proposes blockchain-based aggregation with randomized mining-node selection, multiple pseudonyms, and Bloom-filter authentication; it reports protected individual data, blockchain-verified records, and lower authentication complexity than a conventional scheme.
Problem
Near real-time smart-meter data can reveal users’ behaviors and future activity plans, while aggregation still faces trusted-party, pseudonym-linkage, and authentication-speed challenges.
Method
The scheme uses shared blockchain records, randomly selected mining nodes, multiple pseudonyms, and Bloom filters with zero-knowledge proofs for privacy-preserving aggregation and authentication.
Results
The scheme prevents disclosure of individual data to out-of-group adversaries, verifies blockchain records collectively, and has much lower authentication time complexity than a conventional scheme without Bloom filters.
Takeaways & Limitations
Blockchain integrity supports accurate billing, while group-level aggregation and randomized mining-node selection protect individual data during smart-grid communication.
Abstract
from arXiv · showhide
Intelligence is one of the most important aspects in the development of our future communities. Ranging from smart home, smart building, to smart city, all these smart infrastructures must be supported by intelligent power supply. Smart grid is proposed to solve all challenges of future electricity supply. In smart grid, in order to realize optimal scheduling, a Smart Meter (SM) is installed at each home to collect the near real-time electricity consumption data, which can be used by the utilities to offer better smart home services. However, the near real-time data may disclose user's privacy. An adversary may track the application usage patterns by analyzing the user's electricity consumption profile. In this paper, we propose a privacy-preserving and efficient data aggregation scheme. We divide users into different groups and each group has a private blockchain to record its members' data. To preserve the inner privacy within a group, we use pseudonym to hide user's identity, and each user may create multiple pseudonyms and associate his/her data with different pseudonyms. In addition, the bloom filter is adopted for fast authentication. The analysis shows that the proposed scheme can meet the security requirements, and achieve a better performance than other popular methods.
INTRODUCTION
Smart-grid smart meters provide near real-time data for scheduling and services, but these data can expose household behavior and activity plans. The paper addresses unresolved trust, pseudonym-linkage, and authentication-speed challenges with blockchain, multiple pseudonyms, and bloom-filter authentication.
- Smart meters collect near real-time electricity data that utilities use for scheduling, dynamic pricing, and power services.
- Near real-time consumption data and power requests can reveal appliance-use patterns, user behavior, and periods when a home is unoccupied.
- Existing privacy-preserving approaches face challenges involving trusted aggregation, linking real identities to pseudonyms, and authentication overhead.
- 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.
- Related techniques protect either user identity or user data, while blockchain is adopted here for decentralized data processing.
B. Protecting user’s data
The paper combines blockchain-based data protection with lightweight mining-node selection and bloom-filter authentication. The design avoids proof-of-work’s computational burden while supporting privacy-preserving validation of pseudonyms.
- Household-battery schemes can hide consumption profiles, but privacy depends on battery capacity and may conflict with users’ economic interests.
- Blockchain links blocks through previous-block hashes and Merkle-tree roots, making backdated transaction changes difficult.
- Proof-of-work imposes substantial computational cost because mining repeatedly tests random values until a valid block hash is found.
- 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.
- A bloom filter accelerates authentication by hashing an ID into array positions and checking whether the required positions contain zero.
- A fabricated identifier can be detected when its bloom-filter mapping includes a zero position.
C. Zero knowledge proof
Zero-knowledge proof lets a verifier establish input correctness without learning the input’s content. In the scheme, it validates a pseudonym while preserving the user’s real identity.
- Zero-knowledge proof allows a verifier to prove input correctness without obtaining information about the input content.
- The scheme treats a user’s public key as a pseudonym whose real identity is unknown to other parties.
- The verifier decrypts the signed message and pseudonym with the public key to confirm pseudonym validity without learning the user’s real identity.
A. System model
The system organizes smart meters into consumption-type groups and aggregates their readings through a selected mining node. Group blockchains support center-level planning, dynamic pricing, and billing while KMC manages keys and bloom filters.
- The network uses neighborhood area networks of smart meters and a wide area network carrying each group’s aggregated data to the center unit.
- 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.
- KMC generates multiple key pairs, treats public keys as pseudonyms, and distributes a group-specific bloom filter to users.
- The control center uses aggregated near real-time data to construct electricity profiles for power planning and dynamic pricing.
- The billing center calculates each user’s billing data from the blockchain of that user’s group.
- RSA initialization selects primes p and q, computes n = pq, and derives public and private key parameters for users.
C. Data collection
Users submit electricity-consumption data under pseudonyms, with multiple pseudonyms enabling further obfuscation. Authentication combines signature-based pseudonym authenticity checks with bloom-filter validity checks.
- Each user replaces their true identity with pseudonyms and may bind consumption data to multiple pseudonyms for further obfuscation.
- In each time slot, users randomly split consumption data among different pseudonyms and publish the data, timestamp, pseudonym, and encrypted signature.
- The encrypted message concatenates consumption data, timestamp, and pseudonym, and is encrypted using the sender’s private key.
- Receivers verify pseudonym authenticity by checking whether the sender’s pseudonym correctly decrypts the signature.
- A bloom filter checks pseudonym validity using k hash functions; a pseudonym is accepted when all mapped values are nonzero and otherwise dropped.
E. Selection of mining node
After authentication, users select a mining node from the group based on proximity to the average electricity-consumption data. The selection is difficult for malicious users to manipulate because the average is unknown beforehand.
- After identity authentication, users determine a mining node to aggregate group consumption data and record it on the blockchain.
- Users first calculate the average consumption from received data, then select the user whose data is closest to that average.
- Multiple equally close pseudonyms may all become mining nodes, but they generate the same new block.
- Because no user knows the average before all group data are collected, a malicious user cannot easily increase the probability of selection.
F. Creation of new block
The selected mining node constructs and publishes a blockchain block containing authenticated consumption records. Users verify the block, while the resulting group sums support dispatching, dynamic pricing, and billing.
- The new block is published to group users, who verify its records and attach it to their local blockchain when correct.
- 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.
- If no user disputes the block, the mining node sends the group’s summed consumption data to the control center.
- Each user only needs to verify records related to their own data, and verification can occur offline for real-time dispatch.
- 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.
- Blockchain data integrity enables accurate billing from electricity consumption recorded across different time slots.
SECURITY ANALYSIS
The scheme protects individual data from outside-group adversaries and uses pseudonym authentication to address forgery and registration attacks. Its bloom-filter validity check has a low but nonzero false-acceptance probability.
- An adversary outside the group learns only the summed consumption data, so individual private data are not disclosed.
- The mining-node selection limits an insider’s selection chance because the accurate average is unpredictable before all group data are collected.
- Blockchain records are verified by all group users, providing a check on blocks generated by the mining node.
- Asymmetric encryption and zero-knowledge proof protect pseudonym authenticity because forging a legal pseudonym requires the private key.
- The bloom filter quickly checks whether pseudonyms are registered, rejecting constructed pseudonyms whose mapping values contain a zero.
- Bloom-filter hash collisions create a nonzero error probability, which decreases as the array size increases and may then be ignored.
PERFORMANCE EVALUATION
The scheme is evaluated for authentication time complexity and computational cost against conventional and prior aggregation schemes. The reported results show lower costs for the proposed approach.
- 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.
- Authentication time complexity: Figure 5 compares authentication with and without a bloom filter.
- Computational cost: The proposed scheme has less computational cost than PPM-HAD and DG-APED.
- Computational cost: Figure 6 compares the proposed scheme with PPM-HAD and DG-APED in computational cost.
CONCLUSION
The conclusion presents a blockchain-based aggregation scheme that combines group blockchains, pseudonyms, multiple pseudonyms, and bloom-filter authentication to protect privacy and improve efficiency. Its evaluation reports advantages in computational efficiency, while future work targets authentication overhead during system initialization.
- CONCLUSION: The scheme selects a mining node using differences between users’ consumption data and the average value to support fair selection.
- CONCLUSION: The selected mining node records users’ data in a group blockchain, which is published within the group to ensure message integrity.
- CONCLUSION: Pseudonyms and multiple pseudonyms protect user identities and provide further obfuscation against snooping by group members.
- CONCLUSION: Bloom filters are adopted for fast authentication, and evaluation reports significant computational-efficiency advantages over conventional and other popular aggregation schemes.
- CONCLUSION: Future work will reduce authentication-related computational overhead, especially during system initialization.
BIOGRAPHIES
The biographies describe researchers affiliated with North China Electric Power University, Temple University, and other institutions. Their stated interests span smart-grid security, wireless and network security, cloud security, data analysis, and related areas.
- Author biographies: Zhitao Guan is an associate professor at North China Electric Power University whose research focuses on smart-grid, wireless, and cloud security.
- Author biographies: Guanlin Si is a master’s candidate at North China Electric Power University researching smart-grid security.
- Author biographies: Xiaosong Zhang is a professor at the University of Electronic Science and Technology of China researching network and information technology security and applications.
- Author biographies: Longfei Wu is an assistant professor at Fayetteville State University whose research includes security and privacy issues in mobile devices.
- Author biographies: Nadra Guizani lectures at Gonzaga University and studies disease-spread prediction, mobile networking, large-data analysis, and prediction techniques.
- Author biographies: Xiaojiang Du is a Temple University professor whose research interests include wireless networks, security, and systems.