Privacy-preserving Approach Research Articles

A Privacy-Preserving Approach Against Eavesdropping Attacks on Remote State Estimation for Cyber–Physical Systems

A Privacy-Preserving Approach Against Eavesdropping Attacks on Remote State Estimation for Cyber–Physical Systems

An Efficient Edge–Cloud Partitioning of Random Forests for Distributed Sensor Networks

Intelligent edge sensors that augment legacy “unintelligent” manufacturing systems provides cost-effective functional upgrades. However, the limited compute at these edge devices requires trade-offs in efficient edge-cloud partitioning and raises data privacy issues. This work explores policies for partitioning random forest approaches, which are widely used for inference tasks in smart manufacturing, among sets of devices with different resources and data visibility. We demonstrate, using both publicly available datasets and a real-world grinding machine deployment, that our privacy-preserving approach to partitioning and training offers superior latency-accuracy tradeoffs to purely on-edge computation while still achieving much of the benefits from data-sharing cloud offload strategies.

PriMonitor: An adaptive tuning privacy-preserving approach for multimodal emotion detection

PriMonitor: An adaptive tuning privacy-preserving approach for multimodal emotion detection

A collaborative privacy-preserving approach for passenger demand forecasting of autonomous taxis empowered by federated learning in smart cities

The concept of Autonomous Taxis (ATs) has witnessed a remarkable surge in popularity in recent years, paving the way toward future smart cities. However, accurately forecasting passenger demand for ATs remains a significant challenge. Traditional approaches for passenger demand forecasting often rely on centralized data collection and analysis, which can raise privacy concerns and incur high communication costs. To address these challenges, We propose a collaborative model using Federated Learning (FL) for passenger demand forecasting in smart city transportation systems. Our proposed approach enables ATs in different regions of the smart city to collaboratively learn and improve their demand forecasting models through FL while preserving the privacy of passenger data. We use several backpropagation neural networks as local models for collaborating to train the global model without directly sharing their data. The local model shares only the model updates with a global model that aggregates them, which is then sent back to local models to improve them. Our collaborative approach reduces privacy concerns and communication costs by facilitating learning from each other’s data without direct data sharing. We evaluate our approach using a real-world dataset of over 4500 taxis in Bangkok, Thailand. By utilizing MATLAB2022b, the proposed approach is compared with popular baseline methods and existing research on taxi demand forecasting systems. Results demonstrate that our proposed approach outperforms in passenger demand forecasting, surpassing existing methods in terms of model accuracy, privacy preservation, and performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-squared (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document}). Furthermore, our approach exhibits improved performance over time through the collaborative learning process as more data becomes available.

A security and privacy preserving approach based on social IoT evolving encoding using convolutional neural network

One of the most popular technological frameworks of the year is without a certain Internet of Things (IoT). It permeates numerous industries and has a profound impact on people's lives in all spheres. The “Internet of everything” age is by the IoT technology's rapid development, but it also alters the function of terminal equipment at the network's edge. The name “Internet of Things” has evolved as a result enabling things to be intelligent and competent in talking with verified devices (IoT). Between smart devices, social IoT (IoT) devices interact and adopt social networking concepts. It takes a secure connection between the smart gadgets to enable sociability. To determine whether the suggested strategy is practical it is applied to a convolutional neural network (CNN)-based language similarity analysis model in the context. The model created using the encounter training method is more accurate than the original CNN.

A security and privacy preserving approach based on social IoT and classification using DenseNet convolutional neural network

This method is able to synthesize fine-detailed images by the use of a global attention that gives more attention to the words in the textual descriptions. Also we have the deep attention multimodal similarity model (DAMSM) that calculates the matching loss in the generator. Though this work produced images of high quality, there was some loss while training the system and it takes enough time for training. Although there has been little study on applying character-level Dense Net algorithms for text classification tasks; the Dense Net structures we suggested in this paper have shown outstanding performance in image classification tasks. Extensive testing has revealed that they perform better when it comes to their ability to withstand interruption and that they can influence exerted many organizations implementing information usage and language information on the specifications of user privacy protection, framework implies, and regulatory requirements.

2P3FL: A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

2P3FL: A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

A Privacy-Preserving Approach Using Deep Learning Models for Diabetic Retinopathy Diagnosis

A Privacy-Preserving Approach Using Deep Learning Models for Diabetic Retinopathy Diagnosis

LR-SDP: lightweight privacy preservation approach for distributed mobile devices in IoT environment

LR-SDP: lightweight privacy preservation approach for distributed mobile devices in IoT environment

LR-SDP: Lightweight Privacy Preservation Approach for Distributed Mobile Devices in IoT Environment

LR-SDP: Lightweight Privacy Preservation Approach for Distributed Mobile Devices in IoT Environment

Blockchain-enabled renewable energy certificate trading: A secure and privacy-preserving approach

In the 21st century, transitioning to renewable energy sources is imperative, with fossil fuel reserves depleting rapidly and recognizing critical environmental issues such as climate change, air pollution, water pollution, and habitat destruction. Embracing renewable energy is not only an environmental necessity but also a strategic move with multiple benefits. By shifting to renewable energy sources and supporting their production through the acquisition of renewable energy certificates, we foster innovation and drive economic growth in the renewable energy sector. This, in turn, reduces greenhouse gas emissions, aligning with global efforts to mitigate climate change. Additionally, renewable energy certificates ensure compliance with regulations that mandate the use of renewable energy, enhancing legal adherence while promoting transparency and trust in energy sourcing. To monitor the uptake of renewable energy, governments have implemented Renewable Energy Certificates (RECs) as a tracking mechanism for the production and consumption of renewable energy. They certify the generation of a specific amount of electricity from renewable sources, allowing for accurate tracking of renewable energy contributions to the overall energy mix. However, there are two main challenges to the existing REC schema: (1) The RECs have not been globally adopted due to inconsistent design; (2) The consumer privacy has not been well incorporated in the design of blockchain. In this study, we investigate the trading of RECs between suppliers and consumers using the directed acyclic graph (DAG) blockchain system and introduce a trading schema to help protect consumer information. The DAG system reduces the intense calculation of typical blockchains for scalability and lowers mining fees by eliminating the mining of blocks. The proposed approach allows renewable energy suppliers and consumers to trade RECs globally and to take advantage of secure trading transactions. Our results demonstrate lower transaction time by 41% and energy consumption by 65% compared to proof-of-stake.

A pipeline approach for privacy preservation against poisoning attacks in a Mobile Edge Computing environment

Mobile Edge Computing (MEC) has become increasingly popular due to its ability to provide real-time data processing and improved user experience. However, it also introduces potential security vulnerabilities, as malicious actors could exploit users’ position data stored in the MEC server. To address this challenge, it is essential to develop privacy-preserving schemes that are resistant to malicious activities, such as poisoning attacks. Previous works have not considered the evaluation of such schemes against certain types of poisoning attacks while ensuring sufficient utility, privacy, and scalability during the generation process of poisoning locations. Indeed, meeting the needs of the user or customers, keeping data secure and confidential, and having capacity to accommodate changes in demand are essential for any application or system to operate efficiently and securely. To address this limitation, we present a new pipeline approach for a privacy-preserving scheme against poisoning attacks in the MEC environment. We evaluate this method with a type of poisoning attack that ensures sufficient utility, privacy, and scalability during the generation process and assess its effectiveness in providing security and privacy. Our approach is evaluated on two data sets and the results are compared with three baseline approaches. The results show that our approach is more effective in detecting poisoning locations than the existing works without affecting the QoS metrics (bandwidth, resources consumption, latency and the storage) with should be consider when evaluating MEC method.

Deep Neural Network and GAN-Based Reversible Data Hiding in Encrypted Images: A Privacy-Preserving Approach

Deep Neural Network and GAN-Based Reversible Data Hiding in Encrypted Images: A Privacy-Preserving Approach

Neural Network Based Data Encryption: A Comparison Study among DES, AES, and HE Techniques

With the improvement of technology and the continuous expansion and deepening of neural network technology, its application in computer network security plays an important role. However, the development of neural networks is accompanied by new threats and challenges. This paper proposes to encrypt the weight data using encryption algorithms and embed image encryption algorithms to improve protected data security further. The purpose is to address the feasibility and effectiveness of using modern encryption algorithms for data encryption in machine learning in response to data privacy breaches. The approach consists of training a neural network to simulate a model of machine learning and then encrypting it using Data Encryption Standard (DES), Advanced Encryption Standard (AES), and Homomorphic Encryption (HE) techniques, respectively. Its performance is evaluated based on the encryption/decryption accuracy and computational efficiency. The results indicate that combining DES with Blowfish offers moderate encryption and decryption speeds but is less secure than AES and HE. AES provides a practical solution, balancing security and performance, offering a relatively swift encryption and decryption process while maintaining high security. However, Fernet and HE present a viable alternative if data privacy is a top priority. Encryption and decryption times increase with file size and require sufficient computational resources. Future research should explore image encryption techniques to balance security and accurate image retrieval during decryption. Advanced privacy-preserving approaches, such as differential privacy and secure multi-party computation, may enhance security and confidentiality in digital encryption and decryption processes.

PrSChain: A Blockchain Based Privacy Preserving Approach for Data Service Composition

PrSChain: A Blockchain Based Privacy Preserving Approach for Data Service Composition

FLIBD: A Federated Learning-Based IoT Big Data Management Approach for Privacy-Preserving over Apache Spark with FATE

In this study, we introduce FLIBD, a novel strategy for managing Internet of Things (IoT) Big Data, intricately designed to ensure privacy preservation across extensive system networks. By utilising Federated Learning (FL), Apache Spark, and Federated AI Technology Enabler (FATE), we skilfully investigated the complicated area of IoT data management while simultaneously reinforcing privacy across broad network configurations. Our FLIBD architecture was thoughtfully designed to safeguard data and model privacy through a synergistic integration of distributed model training and secure model consolidation. Notably, we delved into an in-depth examination of adversarial activities within federated learning contexts. The Federated Adversarial Attack for Multi-Task Learning (FAAMT) was thoroughly assessed, unmasking its proficiency in showcasing and exploiting vulnerabilities across various federated learning approaches. Moreover, we offer an incisive evaluation of numerous federated learning defence mechanisms, including Romoa and RFA, in the scope of the FAAMT. Utilising well-defined evaluation metrics and analytical processes, our study demonstrated a resilient framework suitable for managing IoT Big Data across widespread deployments, while concurrently presenting a solid contribution to the progression and discussion surrounding defensive methodologies within the federated learning and IoT areas.

Novel Edge Computing-Based Privacy-Preserving Approach for Smart Healthcare Systems in the Internet of Medical Things

Novel Edge Computing-Based Privacy-Preserving Approach for Smart Healthcare Systems in the Internet of Medical Things

The BioRef Infrastructure, a Framework for Real-Time, Federated, Privacy-Preserving, and Personalized Reference Intervals: Design, Development, and Application.

Reference intervals (RIs) for patient test results are in standard use across many medical disciplines, allowing physicians to identify measurements indicating potentially pathological states with relative ease. The process of inferring cohort-specific RIs is, however, often ignored because of the high costs and cumbersome efforts associated with it. Sophisticated analysis tools are required to automatically infer relevant and locally specific RIs directly from routine laboratory data. These tools would effectively connect clinical laboratory databases to physicians and provide personalized target ranges for the respective cohort population. This study aims to describe the BioRef infrastructure, a multicentric governance and IT framework for the estimation and assessment of patient group-specific RIs from routine clinical laboratory data using an innovative decentralized data-sharing approach and a sophisticated, clinically oriented graphical user interface for data analysis. A common governance agreement and interoperability standards have been established, allowing the harmonization of multidimensional laboratory measurements from multiple clinical databases into a unified "big data" resource. International coding systems, such as the International Classification of Diseases, Tenth Revision (ICD-10); unique identifiers for medical devices from the Global Unique Device Identification Database; type identifiers from the Global Medical Device Nomenclature; and a universal transfer logic, such as the Resource Description Framework (RDF), are used to align the routine laboratory data of each data provider for use within the BioRef framework. With a decentralized data-sharing approach, the BioRef data can be evaluated by end users from each cohort site following a strict "no copy, no move" principle, that is, only data aggregates for the intercohort analysis of target ranges are exchanged. The TI4Health distributed and secure analytics system was used to implement the proposed federated and privacy-preserving approach and comply with the limitations applied to sensitive patient data. Under the BioRef interoperability consensus, clinical partners enable the computation of RIs via the TI4Health graphical user interface for query without exposing the underlying raw data. The interface was developed for use by physicians and clinical laboratory specialists and allows intuitive and interactive data stratification by patient factors (age, sex, and personal medical history) as well as laboratory analysis determinants (device, analyzer, and test kit identifier). This consolidated effort enables the creation of extremely detailed and patient group-specific queries, allowing the generation of individualized, covariate-adjusted RIs on the fly. With the BioRef-TI4Health infrastructure, a framework for clinical physicians and researchers to define precise RIs immediately in a convenient, privacy-preserving, and reproducible manner has been implemented, promoting a vital part of practicing precision medicine while streamlining compliance and avoiding transfers of raw patient data. This new approach can provide a crucial update on RIs and improve patient care for personalized medicine.

Save The Implicit Flow? Enabling Privacy-Preserving RP Authentication in OpenID Connect

OpenID Connect (OIDC) is a Single Sign-On (SSO) protocol that allows users to authenticate to various Relying Parties (RPs) via an Identity Provider (IdP). The main drawback of SSO is its lack of privacy, as the IdP learns the RP’s identity at each user’s login. OIDC supports several protocol flows, of which only one, the Implicit Flow, gives hope for any privacy, as it does not require direct communication between the IdP and RP. This design was initially intended for RPs with technical limitations that prevent them from storing credentials and thus authenticating to the IdP. However, RP authentication is crucial to ensure that users only access properly registered RPs. As a result, the Implicit Flow is being discussed to be excluded from the OAuth specification on which OIDC is based. This paper demonstrates a privacy-preserving approach incorporating RP authentication into the Implicit Flow. The IdP can restrict its service to authenticated RPs and tie each authentication token to a specific user and RP without acquiring knowledge of which user is accessing which RP. We formally define the desired security and privacy properties of such an authenticated Implicit Flow, propose a provably secure construction from generic building blocks, and report on an implementation of our scheme

A lightweight privacy-preserving scheme using pixel block mixing for facial image classification in deep learning

The training of state-of-the-art deep learning models generally requires significant high-quality data, including personal and sensitive data. To ensure privacy of the sensitive data used in training deep learning models, many methods have been designed by the research community. However, it has been observed that many privacy-preserving approaches for image-based deep learning model training incur significant time in the processing of images and/or have low accuracy on trained models when a large number of images is used for classification tasks. Hence, in this paper, we propose a lightweight and efficient approach to preserve image privacy while maintaining the availability of the training set. Specifically, we design the pixel block mixing algorithm for facial image classification privacy preservation in deep learning. Experimental findings show that the models trained by the mixed training set generated by the proposed algorithm maintain their availability. The comparison results of the structural similarity index measure between images in the new training set and the original training set show that our scheme preserves image privacy. Our evaluations also reveal that the data augmentation can be applied to the mixed training set to improve the training effectiveness. We also demonstrate it is computationally challenging for attackers to restore the mixed training set to the original one.