RIOT-based smart metering system for privacy-preserving data aggregation using watermarking and encryption

• URL: http://arxiv.org/abs/2501.06161v2
• Date: Mon, 13 Jan 2025 16:58:59 GMT
• Title: RIOT-based smart metering system for privacy-preserving data aggregation using watermarking and encryption
• Authors: Farzana Kabir, David Megias, Krzysztof Cabaj,
• Abstract summary: This study proposes a privacy-preserving data aggregation protocol that uses reversible watermarking and AES cryptography. There are two versions of the protocol: one for low-frequency smart meters that uses LSB-shifting-based reversible watermarking (RLS) and another for high-frequency smart meters that uses difference expansion-based reversible watermarking (RDE)
• Score: 0.0
• License:
• Abstract: The remarkable advancement of smart grid technology in the IoT sector has raised concerns over the privacy and security of the data collected and transferred in real-time. Smart meters generate detailed information about consumers' energy consumption patterns, increasing the risks of data breaches, identity theft, and other forms of cyber attacks. This study proposes a privacy-preserving data aggregation protocol that uses reversible watermarking and AES cryptography to ensure the security and privacy of the data. There are two versions of the protocol: one for low-frequency smart meters that uses LSB-shifting-based reversible watermarking (RLS) and another for high-frequency smart meters that uses difference expansion-based reversible watermarking (RDE). This enables the aggregation of smart meter data, maintaining confidentiality, integrity, and authenticity. The proposed protocol significantly enhances privacy-preserving measures for smart metering systems, conducting an experimental evaluation with real hardware implementation using Nucleo microcontroller boards and the RIOT operating system and comparing the results to existing security schemes.

Related papers

• Dataset Protection via Watermarked Canaries in Retrieval-Augmented LLMs [67.0310240737424]
  We introduce a novel approach to safeguard the ownership of text datasets and effectively detect unauthorized use by the RA-LLMs. Our approach preserves the original data completely unchanged while protecting it by inserting specifically designed canary documents into the IP dataset. During the detection process, unauthorized usage is identified by querying the canary documents and analyzing the responses of RA-LLMs.
  arXiv  Detail & Related papers  (2025-02-15T04:56:45Z)
• Privacy-Preserving Power Flow Analysis via Secure Multi-Party Computation [1.8006898281412764]
  We show how to perform power flow analysis on cryptographically hidden prosumer data. We analyze the security of our approach in the universal composability framework.
  arXiv  Detail & Related papers  (2024-11-21T20:04:16Z)
• Defining 'Good': Evaluation Framework for Synthetic Smart Meter Data [14.779917834583577]
  We show that standard privacy attack methods are inadequate for assessing privacy risks of smart meter datasets. We propose an improved method by injecting training data with implausible outliers, then launching privacy attacks directly on these outliers.
  arXiv  Detail & Related papers  (2024-07-16T14:41:27Z)
• Improving Privacy-Preserving Techniques for Smart Grid using Lattice-based Cryptography [1.4856472820492366]
  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.
  arXiv  Detail & Related papers  (2024-04-17T19:51:52Z)
• Privacy Preserving Anomaly Detection on Homomorphic Encrypted Data from IoT Sensors [0.9831489366502302]
  Homomorphic encryption schemes are promising solutions as they enable the processing and execution of operations on IoT data while still encrypted. We propose a novel privacy-preserving anomaly detection solution designed for homomorphically encrypted data generated by IoT devices.
  arXiv  Detail & Related papers  (2024-03-14T12:11:25Z)
• Safe and Robust Watermark Injection with a Single OoD Image [90.71804273115585]
  Training a high-performance deep neural network requires large amounts of data and computational resources. We propose a safe and robust backdoor-based watermark injection technique. We induce random perturbation of model parameters during watermark injection to defend against common watermark removal attacks.
  arXiv  Detail & Related papers  (2023-09-04T19:58:35Z)
• Zero Trust Real-Time Lightweight Access Control Protocol for Mobile Cloud-Based IoT Sensors [0.0]
  Zero Trust Architecture enhances IoT security by challenging conventional trust models. This paper presents a new zero-trust real-time lightweight access control protocol for Cloud-centric dynamic IoT sensor networks.
  arXiv  Detail & Related papers  (2023-09-04T00:11:45Z)
• Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMO [56.022764337221325]
  Reconfigurable intelligent surface (RIS) can significantly enhance the service coverage of Tera-Hertz massive multiple-input multiple-output (MIMO) communication systems. However, obtaining accurate high-dimensional channel state information (CSI) with limited pilot and feedback signaling overhead is challenging. This paper proposes a deep learning (DL)-based rate-splitting multiple access scheme for RIS-aided Tera-Hertz multi-user multiple access systems.
  arXiv  Detail & Related papers  (2022-09-18T03:07:37Z)
• Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones [57.468730437381076]
  We present a Semi-supervised Federated Learning (SSFL) framework for privacy-preserving UAV image recognition. There are significant differences in the number, features, and distribution of local data collected by UAVs using different camera modules. We propose an aggregation rule based on the frequency of the client's participation in training, namely the FedFreq aggregation rule.
  arXiv  Detail & Related papers  (2022-01-03T16:49:33Z)
• Reconciling Security and Utility in Next-Generation Epidemic Risk Mitigation Systems [49.05741109401773]
  We present Silmarillion, a system that reconciles user's privacy with rich data collection for higher utility. In Silmarillion, user devices record Bluetooth encounters with beacons installed in strategic locations. We describe the design of Silmarillion and its communication protocols that ensure user privacy and data security.
  arXiv  Detail & Related papers  (2020-11-16T16:19:37Z)
• DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection [98.43451011898212]
  In multiuser multiple-input multiple-output (MIMO) setups, where multiple symbols are simultaneously transmitted, accurate symbol detection is challenging. We propose a data-driven implementation of the iterative soft interference cancellation (SIC) algorithm which we refer to as DeepSIC. DeepSIC learns to carry out joint detection from a limited set of training samples without requiring the channel to be linear.
  arXiv  Detail & Related papers  (2020-02-08T18:31:00Z)

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.