Privacy-preserving Data Research Articles

Employing Cryptographic Techniques for Data Security and Privacy Preservation

Employing Cryptographic Techniques for Data Security and Privacy Preservation

Big Data and Privacy with a focus on Statistical Approaches to Ensuring Data Confidentiality

In the era of Big Data, where vast datasets fuel innovation across industries, the paramount concern remains safeguarding individual privacy. This article explores how statistical approaches ensure data confidentiality amidst the proliferation of digital information. Differential privacy techniques introduce calibrated noise to protect identities while preserving data utility, crucial for compliance and trust in data-driven decision-making. Secure Multiparty Computation (MPC) enables collaborative analysis without exposing raw data, supporting privacy in sectors like healthcare and finance. Privacy-preserving data mining techniques integrate encryption and anonymization to extract insights while shielding sensitive information. Anonymization and de-identification methods further bolster privacy by masking identifiable data, essential for adhering to stringent regulations like GDPR and HIPAA. As data generation escalates, advancing these statistical methods is essential for maintaining privacy integrity in the evolving landscape of Big Data applications. Keywords: Big Data, Privacy, Statistical approaches, Differential privacy, Secure multiparty computation.

Reinforcement prompting for financial synthetic data generation

The emergence of Large Language Models (LLMs) has unlocked unprecedented potential for comprehending and generating human-like text, fueling advances in the finance domain – a tool that can shape investment strategies and market predictions. Nevertheless, challenges stemming from the necessity for extensive labeled data and the imperative for data privacy remain. The generation of high-quality synthetic data emerges as a promising avenue to circumvent these issues. In this paper, we introduce a novel methodology, named “Reinforcement Prompting”, to address these challenges. Our strategy employs a policy network as a Selector to generate prompts, and an LLM as an Executor to produce financial synthetic data. This synthetic data generation process preserves data privacy and mitigates the dependency on real-world labeled datasets. We validate the effectiveness of our approach through experimental evaluations. Our results indicate that models trained on synthetic data generated via our approach exhibit competitive performance when compared to those trained on actual financial data, thereby bridging the performance gap. This research provides a novel solution to the challenges of data privacy and labeled data scarcity in financial sentiment analysis, offering considerable advancement in the field of financial machine learning.

A Differentially Privacy Assisted Federated Learning Scheme to Preserve Data Privacy for IoMT Applications

A Differentially Privacy Assisted Federated Learning Scheme to Preserve Data Privacy for IoMT Applications

HyObscure: Hybrid Obscuring for Privacy-Preserving Data Publishing

HyObscure: Hybrid Obscuring for Privacy-Preserving Data Publishing

A Fair Contribution Measurement Method for Federated Learning.

Federated learning is an effective approach for preserving data privacy and security, enabling machine learning to occur in a distributed environment and promoting its development. However, an urgent problem that needs to be addressed is how to encourage active client participation in federated learning. The Shapley value, a classical concept in cooperative game theory, has been utilized for data valuation in machine learning services. Nevertheless, existing numerical evaluation schemes based on the Shapley value are impractical, as they necessitate additional model training, leading to increased communication overhead. Moreover, participants' data may exhibit Non-IID characteristics, posing a significant challenge to evaluating participant contributions. Non-IID data have greatly affected the accuracy of the global model, weakened the marginal effect of the participants, and led to the underestimated contribution measurement results of the participants. Current work often overlooks the impact of heterogeneity on model aggregation. This paper presents a fair federated learning contribution measurement scheme that addresses the need for additional model computations. By introducing a novel aggregation weight, it enhances the accuracy of the contribution measurement. Experiments on the MNIST and Fashion MNIST dataset show that the proposed method can accurately compute the contributions of participants. Compared to existing baseline algorithms, the model accuracy is significantly improved, with a similar time cost.

Applications of Homomorphic Encryption in Secure Computation

Background Homomorphic encryption (HE) represents a pivotal innovation in modern cryptography, offering a pathway to secure computation on encrypted data. This paper embarks on a comprehensive exploration of HE's applications, elucidating its transformative potential in bolstering data security and privacy across various domains. Methods The research employs a mixed-methods approach to evaluate HE technologies. Quantitatively, it develops realistic datasets simulating healthcare and financial data, assessing HE's performance in encrypted computations. Various encryption schemes are rigorously tested for efficiency and accuracy under different conditions. Qualitatively, insights from expert interviews and case studies of HE implementations provide additional context on practical challenges and strategic benefits. Results The simulations and analyses showcase the efficiency, scalability, and security of HE techniques in diverse scenarios. The empirical evidence validates the real-world applicability of HE, demonstrating its versatility and efficacy in secure computation outsourcing, privacy-preserving data analysis, and secure multi-party computation. Conclusions This research paper highlights the transformative power of homomorphic encryption, advocating for its widespread adoption and integration. By bridging the gap between theoretical understanding and practical implementation, the paper contributes to advancing secure computation practices, addressing contemporary challenges in data security and privacy amidst evolving cybersecurity threats and the increasing ubiquity of sensitive data. In essence, this research serves as a beacon of insight into the future of data confidentiality and integrity, promoting HE as a crucial tool for revolutionizing the landscape of data security and privacy in an interconnected world.

Dynamical analysis of high-order Hopfield neural network with application in WBANs

Given the increasing demand for privacy preservation of medical data, a novel medical image encryption scheme is proposed based on Invertible Neural Network (INN) in this paper. Firstly, a new High-Order Hopfield Neural Network (HOHNN) is designed to yield a cipher flow as the conditional input, thereby regulating the encryption process. Moreover, its intrinsic properties as well as various dynamic behaviors are both demonstrated through theoretical analysis and two-parameter Lyapunov exponential charts. Secondly, guided by a multi-objective loss function and a known prior distribution, the original medical image is encrypted into a noise-like cipher image through the proposed encryption scheme. Besides, it is worth mentioning that unlike the existing deep learning-based data encryption schemes, the decryption network shares the identical weight matrix and network structure with the corresponding encryption one in this work. Finally, extensive simulation experiments have validated the feasibility as well as the security of the proposed scheme.

A comprehensive survey on client selection strategies in federated learning

Federated learning (FL) has emerged as a promising paradigm for collaborative model training while preserving data privacy. Client selection plays a crucial role in determining the overall performance and efficiency of the FL training process. However, the heterogeneity of clients in terms of computational resources, data quality, and network conditions poses significant challenges in selecting the optimal set of clients for each training round. This survey provides a comprehensive review of client selection methodologies in FL, addressing the challenges and opportunities in this field. We present a systematic categorization of existing client selection techniques based on their underlying principles and objectives, and discuss the key challenges, including resource constraints, data heterogeneity, and security concerns. Additionally, we provide a comparative analysis of the surveyed techniques, highlighting their strengths, limitations, and suitability for various FL scenarios. Furthermore, we identify open research problems and propose future research directions, emphasizing the need for more efficient, adaptive, and secure selection strategies. This survey serves as a comprehensive framework for understanding client selection in FL, bridging gaps in the existing literature and providing guidance for future research, including ethical considerations and domain-specific applications. Our work aims to support practitioners in making informed decisions and to stimulate further research in this critical area of FL.

Robust Estimation Method against Poisoning Attacks for Key-Value Data with Local Differential Privacy

Local differential privacy (LDP) protects user information from potential threats by randomizing data on individual devices before transmission to untrusted collectors. This method enables collectors to derive user statistics by analyzing randomized data, thereby presenting a promising avenue for privacy-preserving data collection. In the context of key–value data, in which discrete and continuous values coexist, PrivKV has been introduced as an LDP protocol to ensure secure collection. However, this framework is susceptible to poisoning attacks. To address this vulnerability, we propose an expectation maximization (EM)-based algorithm combined with a cryptographic protocol to facilitate secure random sampling. Our LDP protocol, known as emPrivKV, exhibits two key advantages: it improves the accuracy of statistical information estimation from randomized data, and enhances resilience against the manipulation of statistics, that is, poisoning attacks. These attacks involve malicious users manipulating the analysis results without detection. This study presents the empirical results of applying the emPrivKV protocol to both synthetic and open datasets, highlighting a notable improvement in the precision of statistical value estimation and robustness against poisoning attacks. As a result, emPrivKV improved the frequency and the mean gains by 17.1% and 25.9%, respectively, compared to PrivKV, with the number of fake users being 0.1 of the genuine users. Our findings contribute to the ongoing discourse on refining LDP protocols for key–value data in scenarios involving privacy-sensitive information.

A prototype-assisted clustered federated learning for big data security and privacy preservation

In the rapidly expanding field of IoT, data production has reached an unprecedented scale, providing valuable insights that accelerate decision-making processes. However, ensuring the privacy and security of this massive amount of data poses significant challenges. In this paper, we propose using clustered federated learning (CFL) as a solution to ensure both the security and privacy of big data by uploading model weights while keeping the data stored locally. Nevertheless, there are practical challenges in applying CFL to big data: (1) the participating FL clients are unlikely to have identical data distributions; (2) insufficient attention is given to the similarity between different clusters; and (3) CFL tends to ignore the class imbalance problem (i.e., long-tailed), which hinders its application in big data and affects the quality of target tasks. To address these issues and enable widespread CFL deployment in big data applications, this paper proposes a prototype-assisted clustered federated learning framework (MDSPFL). It relaxes the assumption of unique data distribution for each client, allowing the client’s local dataset to follow multiple source distributions considering classification class imbalance, thereby aligning with clients in a big data environment. Specifically, MDSPFL employs the proximal update mechanism to handle workload surges caused by mixed distribution and unavailability of similarity between cluster models. Additionally, MDSPFL introduces a class-balanced local training mechanism to resolve the long-tailed problem, which utilizes contrastive learning and class prototypes to enforce a uniform distribution of all classes in the feature space. We conduct extensive experiments on different datasets (EMNIST, Cifar10, Cifar100), and the experimental results demonstrate the effectiveness of our proposed MDSPFL in big data scenarios with imbalance and mixed-distribution clients.

Federated quantum long short-term memory (FedQLSTM)

Quantum federated learning (QFL) can facilitate collaborative learning across multiple clients using quantum machine learning (QML) models, while preserving data privacy. Although recent advances in QFL span different tasks like classification while leveraging several data types, no prior work has focused on developing a QFL framework that utilizes temporal data to approximate functions useful to analyze the performance of distributed quantum sensing networks. In this paper, a novel QFL framework that is the first to integrate quantum long short-term memory (QLSTM) models with temporal data is proposed. The proposed federated QLSTM (FedQLSTM) framework is exploited for performing the task of function approximation. In this regard, three key use cases are presented: Bessel function approximation, sinusoidal delayed quantum feedback control function approximation, and Struve function approximation. Simulation results confirm that, for all considered use cases, the proposed FedQLSTM framework achieves a faster convergence rate under one local training epoch, minimizing the overall computations, and saving 25–33% of the number of communication rounds needed until convergence compared to an FL framework with classical LSTM models.

Electronic Health Record-Oriented Knowledge Graph System for Collaborative Clinical Decision Support Using Multicenter Fragmented Medical Data: Design and Application Study.

The medical knowledge graph provides explainable decision support, helping clinicians with prompt diagnosis and treatment suggestions. However, in real-world clinical practice, patients visit different hospitals seeking various medical services, resulting in fragmented patient data across hospitals. With data security issues, data fragmentation limits the application of knowledge graphs because single-hospital data cannot provide complete evidence for generating precise decision support and comprehensive explanations. It is important to study new methods for knowledge graph systems to integrate into multicenter, information-sensitive medical environments, using fragmented patient records for decision support while maintaining data privacy and security. This study aims to propose an electronic health record (EHR)-oriented knowledge graph system for collaborative reasoning with multicenter fragmented patient medical data, all the while preserving data privacy. The study introduced an EHR knowledge graph framework and a novel collaborative reasoning process for utilizing multicenter fragmented information. The system was deployed in each hospital and used a unified semantic structure and Observational Medical Outcomes Partnership (OMOP) vocabulary to standardize the local EHR data set. The system transforms local EHR data into semantic formats and performs semantic reasoning to generate intermediate reasoning findings. The generated intermediate findings used hypernym concepts to isolate original medical data. The intermediate findings and hash-encrypted patient identities were synchronized through a blockchain network. The multicenter intermediate findings were collaborated for final reasoning and clinical decision support without gathering original EHR data. The system underwent evaluation through an application study involving the utilization of multicenter fragmented EHR data to alert non-nephrology clinicians about overlooked patients with chronic kidney disease (CKD). The study covered 1185 patients in nonnephrology departments from 3 hospitals. The patients visited at least two of the hospitals. Of these, 124 patients were identified as meeting CKD diagnosis criteria through collaborative reasoning using multicenter EHR data, whereas the data from individual hospitals alone could not facilitate the identification of CKD in these patients. The assessment by clinicians indicated that 78/91 (86%) patients were CKD positive. The proposed system was able to effectively utilize multicenter fragmented EHR data for clinical application. The application study showed the clinical benefits of the system with prompt and comprehensive decision support.

Blockchain-enabled data sharing for IoT: A lightweight, secure and searchable scheme

With the popularity of Internet of Things (IoT) applications, data sharing among IoT devices has become more frequent. Many researchers have proposed fine-grained access control schemes and searchable encryption algorithms to support privacy-preserving data sharing. But there still exist two challenges. First, the existing schemes are computationally intensive, making them unsuitable for resource-limited IoT terminals. Second, they rely on a central server to honestly conduct search operations, making them unable to tackle malicious servers. To this end, we propose a blockchain-enabled data-sharing scheme for IoT that supports lightweight, secure, and searchable data sharing. We design an outsourcing attribute-based encryption algorithm to alleviate the overhead of resource-limited IoT terminals, which reduces the local encryption and decryption to a constant level. Instead of relying on central servers, we leverage smart contract to execute ciphertext retrieval, ensuring the accuracy of search results. Formal security analysis demonstrates that the proposed scheme is chosen-plaintext secure under the DBDH assumption. Experimental simulation shows that it reduces the search time by at least 79.9%, the encryption and decryption complexities to both O(1).

Anomaly detection based on LSTM and autoencoders using federated learning in smart electric grid

In smart electric grid systems, various sensors and Internet of Things (IoT) devices are used to collect electrical data at substations. In a traditional system, a multitude of energy-related data from substations needs to be migrated to central storage, such as Cloud or edge devices, for knowledge extraction that might impose severe data misuse, data manipulation, or privacy leakage. This motivates to propose anomaly detection system to detect threats and Federated Learning to resolve the issues of data silos and privacy of data. In this article, we present a framework to identify anomalies in industrial data that are gathered from the remote terminal devices deployed at the substations in the smart electric grid system. The anomaly detection system is based on Long Short-Term Memory (LSTM) and autoencoders that employs Mean Standard Deviation (MSD) and Median Absolute Deviation (MAD) approaches for detecting anomalies. We deploy Federated Learning (FL) to preserve the privacy of the data generated by the substations. FL enables energy providers to train shared AI models cooperatively without disclosing the data to the server. In order to further enhance the security and privacy properties of the proposed framework, we implemented homomorphic encryption based on the Paillier algorithm for preserving data privacy. The proposed security model performs better with MSD approach using HE-128 bit key providing 97% F1-score and 98% accuracy for K=5 with low computation overhead as compared with HE-256 bit key.

Homomorphic Encryption and Secure Multi-Party Computation: Mathematical Tools for Privacy-Preserving Data Analysis in the Cloud

With more and more people using cloud computing and storing and handling data remotely, protecting the privacy and safety of private data has become very important. Homomorphic encryption and safe multi-party computing (MPC) are two new mathematics tools that offer strong ways to analyze data in the cloud while protecting privacy. When you use homomorphic encryption, you can do calculations directly on protected data, so you can process data securely without having to decode private data. This method makes sure that data stays protected while operations are being done, keeping it safe from people who shouldn't have access to it or seeing it. Cloud service providers can use homomorphic encryption to do different types of analysis on protected data, like collecting, searching, and machine learning, without revealing the private information that lies beneath. Secure multi-party computation protects privacy in situations where multiple people work together to analyze data. MPC allows for joint analysis without letting other people see individual datasets by spreading computations across multiple entities, each of which holds a piece of the data. MPC uses cryptographic protocols and methods to make sure that processes are done without revealing private inputs. This lets multiple people work together to analyze data while keeping privacy. These math tools can be used for many different types of data analysis jobs in the cloud, such as predictive modeling, machine learning, and statistical analysis. They also make it safe for different groups to share and work together on data, like businesses, academics, and people, without putting data protection at risk. There are still problems with how homomorphic encryption and safe MPC can be used in the real world and how they can be scaled up. These problems are mostly related to the amount of work that needs to be done and how efficiently it works. The main goal of ongoing study is to create improved methods and programs that will make these techniques work better and be easier to use in the real world.

A privacy-preserving data aggregation system based on blockchain in VANET

In the realm of vehicular ad hoc networks (VANETs), data aggregation plays a pivotal role in bringing together data from multiple vehicles for further processing and sharing. Erroneous data feedback can significantly impact vehicle operations, control, and overall safety, necessitating the assurance of security in vehicular data aggregation. Addressing the security risks and challenges inherent in data aggregation within VANETs, this paper introduces a blockchain-based scheme for secure and anonymous data aggregation. The proposed scheme integrates cloud computing with blockchain technology, presenting a novel blockchain-based data aggregation system that robustly supports efficient and secure data collection in VANETs. Leveraging key escrow resilience mechanisms, the solution ensures the security of system keys, preventing the security problems caused by keys generated by third parties alone in the past. Furthermore, through secondary data aggregation, fine-grained data aggregation is achieved, providing effective support for cloud services in VANETs. The effectiveness of the proposed scheme is confirmed through security analysis and performance evaluations, demonstrating superior computational and communication efficiency compared existing alternatives.

ANALYSIS OF HOW AND WHY STYLISTIC LANGUAGE IMPACTS ADVERTISING WITH A JOURNALISTIC APPROACH

Objective and Background: The Internet of Things (IoT) devices store a great volume of data. Therefore, with the advancements in technology, some new protection guidelines have to be developed to preserve data privacy and collection. The present study aimed to determine a secure protective framework using the BTD structure in a specified zone, and provide the idea of hiding the sensory data of an operator device to preserve data privacy without losing the data integrity. Methodology: Two simple and enhanced methods were used. The BTD framework and estimated ground truth were used in both modes. The enhanced method allows the users to use a random weight (variance) when combining their sensory data with the estimated ground truth provided by the agent. We ran the BTD framework in the CloudSim (network simulation framework). Each simulation is run for 500 virtual minutes with 12 iterations. The target zone is set to 50×50 meters. The test results were collected on real-world data traces from sensory systems. In addition to the BTD structure, the Voting method has been also used, and to do so, majority voting and CRH truth discovery method (Conflict Resolution on Heterogeneous Data) which does not take any measures to break the security of the sensor during the process is used. Findings: The comparative results show a 0.70-0.71 error level between the BTD and other functions, at least. As long as there is at least one reliable source, the BTD will have an error level of 0. Simulations show that BTD has a worst-case error of 0.05 and a weighted variance of +/- 5%. Based on previous frameworks, the BTD is improved by three requirements of a population identified in the IoT: Preservation of privacy for the device user, data integrity for the data collection group, and low-cost computation on the user device.

A Federated Learning Framework for Brain Tumor Segmentation Without Sharing Patient Data

ABSTRACTBrain tumors pose a significant threat to human health, necessitating early detection and accurate diagnosis to enhance treatment outcomes. However, centralized data collection and processing encounter challenges related to privacy breaches and data integration due to the sensitivity and diversity of brain tumor patient data. In response, this paper proposes an innovative federated learning‐based approach for brain tumor detection, facilitating multicenter data sharing while safeguarding individual data privacy. Our proposed federated learning architecture features each medical center as a participant, with each retaining local data and engaging in secure communication with a central server. Within this federated migration learning framework, each medical center independently trains a base model on its local data and transmits a fraction of the model's parameters to the central server. The central server leverages these parameters for model aggregation and knowledge sharing, facilitating the exchange and migration of models among participating medical centers. This collaborative approach empowers individual medical centers to share knowledge and experiences, thereby enhancing the performance and accuracy of the brain tumor detection model. To validate our federated learning model, we conduct comprehensive evaluations using an independent test dataset, comparing its performance with traditional centralized learning approaches. The experimental results underscore the superiority of the federated learning‐based brain tumor detection approach, achieving heightened detection performance compared with traditional methods while meticulously preserving data privacy. In conclusion, our study presents an innovative solution for effective data collaboration and privacy protection in the realm of brain tumor detection, holding promising clinical applications. The federated learning approach not only advances detection accuracy but also establishes a secure and privacy‐preserving foundation for collaborative research in medical imaging.

Smart Contract Assisted Privacy-Preserving Data Aggregation and Management Scheme for Smart Grid

Smart Contract Assisted Privacy-Preserving Data Aggregation and Management Scheme for Smart Grid