Improving Privacy-Preserving Techniques for Smart Grid using Lattice-based Cryptography
- URL: http://arxiv.org/abs/2404.16865v1
- Date: Wed, 17 Apr 2024 19:51:52 GMT
- Title: Improving Privacy-Preserving Techniques for Smart Grid using Lattice-based Cryptography
- Authors: Saleh Darzi, Bahareh Akhbari, Hassan Khodaiemehr,
- Abstract summary: SPDBlock is a blockchain-based solution ensuring privacy, integrity, and resistance to attacks.
It detects and prosecutes malicious entities while efficiently handling multi-dimensional data transmission.
Performance tests reveal SPDBlock's superiority in communication and computational efficiency over traditional schemes.
- Score: 1.4856472820492366
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Advancements in communication and information tech birthed the Smart Grid, optimizing energy and data transmission. Yet, user privacy is at risk due to frequent data collection. Existing privacy schemes face vulnerability with quantum machines. To tackle this, the LPM2DA scheme is introduced, utilizing lattice-based encryption and signatures for secure data aggregation. It ensures privacy, integrity, and authentication, enabling statistical analysis while preserving user privacy. Traditional aggregation schemes suffer from weak network models and centralization issues. Enter SPDBlock, a blockchain-based solution ensuring privacy, integrity, and resistance to attacks. It detects and prosecutes malicious entities while efficiently handling multi-dimensional data transmission. Through distributed decryption and secret sharing, only valid data can be decrypted with minimal involvement from smart meters. Performance tests reveal SPDBlock's superiority in communication and computational efficiency over traditional schemes.
Related papers
- EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters [3.9660142560142067]
Federated Learning (FL) enables model training across decentralized devices by communicating solely local model updates to an aggregation server.
FL remains vulnerable to inference attacks during model update transmissions.
We present EncCluster, a novel method that integrates model compression through weight clustering with recent decentralized FE and privacy-enhancing data encoding.
arXiv Detail & Related papers (2024-06-13T14:16:50Z) - Privacy-Preserving Data Aggregation Techniques for Enhanced Efficiency and Security in Wireless Sensor Networks: A Comprehensive Analysis and Evaluation [0.0]
We present a multidimensional, highly effective method for aggregating data for wireless sensor networks while maintaining privacy.
The suggested system is resistant to data loss and secure against both active and passive privacy compromising attacks.
arXiv Detail & Related papers (2024-03-29T11:09:22Z) - Blockchain-enabled Data Governance for Privacy-Preserved Sharing of Confidential Data [1.6006586061577806]
We propose a blockchain-based data governance system that employs attribute-based encryption to prevent privacy leakage and credential misuse.
First, our ABE encryption system can handle multi-authority use cases while protecting identity privacy and hiding access policy.
Second, applying the Advanced Encryption Standard (AES) for data encryption makes the whole system efficient and responsive to real-world conditions.
arXiv Detail & Related papers (2023-09-08T05:01:59Z) - Blockchain-empowered Federated Learning for Healthcare Metaverses:
User-centric Incentive Mechanism with Optimal Data Freshness [66.3982155172418]
We first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses.
We then utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing.
arXiv Detail & Related papers (2023-07-29T12:54:03Z) - Breaking the Communication-Privacy-Accuracy Tradeoff with
$f$-Differential Privacy [51.11280118806893]
We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability.
We study the local differential privacy guarantees of discrete-valued mechanisms with finite output space through the lens of $f$-differential privacy (DP)
More specifically, we advance the existing literature by deriving tight $f$-DP guarantees for a variety of discrete-valued mechanisms.
arXiv Detail & Related papers (2023-02-19T16:58:53Z) - Decentralized Stochastic Optimization with Inherent Privacy Protection [103.62463469366557]
Decentralized optimization is the basic building block of modern collaborative machine learning, distributed estimation and control, and large-scale sensing.
Since involved data, privacy protection has become an increasingly pressing need in the implementation of decentralized optimization algorithms.
arXiv Detail & Related papers (2022-05-08T14:38:23Z) - Privacy-Preserving Federated Learning via System Immersion and Random
Matrix Encryption [4.258856853258348]
Federated learning (FL) has emerged as a privacy solution for collaborative distributed learning where clients train AI models directly on their devices instead of sharing their data with a centralized (potentially adversarial) server.
We propose a Privacy-Preserving Federated Learning (PPFL) framework built on the synergy of matrix encryption and system immersion tools from control theory.
We show that our algorithm provides the same level of accuracy and convergence rate as the standard FL with a negligible cost while revealing no information about clients' data.
arXiv Detail & Related papers (2022-04-05T21:28:59Z) - Linear Model with Local Differential Privacy [0.225596179391365]
Privacy preserving techniques have been widely studied to analyze distributed data across different agencies.
Secure multiparty computation has been widely studied for privacy protection with high privacy level but intense cost.
matrix masking technique is applied to encrypt data such that the secure schemes are against malicious adversaries.
arXiv Detail & Related papers (2022-02-05T01:18:00Z) - Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge
Caching [91.50631418179331]
A privacy-preserving distributed deep policy gradient (P2D3PG) is proposed to maximize the cache hit rates of devices in the MEC networks.
We convert the distributed optimizations into model-free Markov decision process problems and then introduce a privacy-preserving federated learning method for popularity prediction.
arXiv Detail & Related papers (2021-10-20T02:48:27Z) - BeeTrace: A Unified Platform for Secure Contact Tracing that Breaks Data
Silos [73.84437456144994]
Contact tracing is an important method to control the spread of an infectious disease such as COVID-19.
Current solutions do not utilize the huge volume of data stored in business databases and individual digital devices.
We propose BeeTrace, a unified platform that breaks data silos and deploys state-of-the-art cryptographic protocols to guarantee privacy goals.
arXiv Detail & Related papers (2020-07-05T10:33:45Z) - CryptoSPN: Privacy-preserving Sum-Product Network Inference [84.88362774693914]
We present a framework for privacy-preserving inference of sum-product networks (SPNs)
CryptoSPN achieves highly efficient and accurate inference in the order of seconds for medium-sized SPNs.
arXiv Detail & Related papers (2020-02-03T14:49:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.