Privacy-preserving Techniques Research Articles

Privacy Challenges and Solutions in Big Data Analytics: A Comprehensive Review

Abstract: Large data collections with a more intricate and diversified structure are referred to as "big data." These traits are typically associated with more challenges when it comes to storing, analyzing, and applying additional procedures or getting results. The technique of analyzing vast quantities of intricate data to uncover patterns or connections that are concealed is known as "big data analytics." Nonetheless, there is a clear conflict between big data's extensive use and its security and privacy. This paper examines privacy needs in big data, distinguishes between privacy and security, and focuses on privacy and security issues in large data. This study addresses the applications of privacy in business by utilizing established techniques like HybrEx, k-anonymity, T-closeness, and L-diversity. Many privacy-preserving techniques have been created for protection of privacy at various phases of the big data life cycle (such as data generation, storage, and processing). This work aims to explain the issues facing current privacy preservation strategies in big data and to give a comprehensive evaluation of them. This study also introduces new approaches to privacy preservation in big data, such as quick anonymization of massive data streams, identity-based anonymization, differential privacy, privacy preserving big data publishing, and hiding a needle in a haystack. This study discusses big data privacy and security issues in healthcare. A comparative analysis of some contemporary large data privacy approaches is also conducted. A comparative analysis of the latest big data privacy approaches is also conducted.

Horizontal Federated Recommender System: A Survey

Due to underlying privacy-sensitive information in user-item interaction data, the risk of privacy leakage exists in the centralized-training recommender system (RecSys). To this issue, federated learning, a privacy-oriented distributed computing paradigm, is introduced and promotes the crossing field “Federated Recommender System (FedRec).” Regarding data distribution characteristics, there are horizontal, vertical, and transfer variants, where horizontal FedRec (HFedRec) occupies a dominant position. User devices can personally participate in the horizontal federated architecture, making user-level privacy feasible. Therefore, we target the horizontal point and summarize existing works more elaborately than existing FedRec surveys. First, from the model perspective, we group them into different learning paradigms (e.g., deep learning and meta learning). Second, from the privacy perspective, privacy-preserving techniques are systematically organized (e.g., homomorphic encryption and differential privacy). Third, from the federated perspective, fundamental issues (e.g., communication and fairness) are discussed. Fourth, each perspective has detailed subcategories, and we specifically state their unique challenges with the observation of current progress. Finally, we figure out potential issues and promising directions for future research.

Privacy-Preserving Data Sharing Platform

In today's data-driven healthcare landscape, the secure sharing of sensitive medical information is essential for improving patient care, facilitating medical research, and advancing healthcare outcomes. However, ensuring the integrity, confidentiality, and privacy of patient data poses significant challenges, particularly in the context of big data environments. This presents a comprehensive framework for privacy-preserving data sharing in healthcare, leveraging a combination of cryptographic techniques, encryption, and secure computation protocols. The framework encompasses various privacy-preserving mechanisms, including Differential Privacy with Data Perturbation, Secure Multi-Party Computation (SMPC), and Homomorphic Encryption, to protect sensitive healthcare data from unauthorized access and disclosure. By implementing state-of-the-art privacy-preserving techniques, the framework aims to enable secure data sharing among multiple parties while complying with regulatory requirements such as HIPAA and GDPR. Additionally, the paper discusses the project scope, which includes cryptography, encryption, decryption, integrity, confidentiality, privacy, policies, procedures, security, and secure data sharing infrastructure. The proposed framework provides a practical solution for healthcare organizations and research institutions to collaborate on data-driven initiatives while safeguarding patient privacy and maintaining trust. Evaluation of the framework's effectiveness and performance metrics is conducted to validate its feasibility and efficacy in real-world healthcare settings. Keywords: Privacy-preserving data sharing, Differential Privacy, Data Perturbation, Secure Multi-Party Computation (SMPC)

Privacy Preservation of Electronic Health Records in the Modern Era: A Systematic Survey

Building a secure and privacy-preserving health data sharing framework is a topic of great interest in the healthcare sector, but its success is subject to ensuring the privacy of user data. We clarified the definitions of privacy, confidentiality and security (PCS) because these three terms have been used interchangeably in the literature. We found that researchers and developers must address the differences of these three terms when developing electronic health record (EHR) solutions. We surveyed 130 studies on EHRs, privacy-preserving techniques, and tools that were published between 2012 and 2022, aiming to preserve the privacy of EHRs. The observations and findings were summarized with the help of the identified studies framed along the survey questions addressed in the literature review. Our findings suggested that the usage of access control, blockchain, cloud-based, and cryptography techniques is common for EHR data sharing. We summarized the commonly used strategies for preserving privacy that are implemented by various EHR tools. Additionally, we collated a comprehensive list of differences and similarities between PCS. Finally, we summarized the findings in a tabular form for all EHR tools and techniques and proposed a fusion of techniques to better preserve the PCS of EHRs.

Emerging Technologies for Automation in Environmental Sensing: Review

This article explores the impact of automation on environmental sensing, focusing on advanced technologies that revolutionize data collection analysis and monitoring. The International Union of Pure and Applied Chemistry (IUPAC) defines automation as integrating hardware and software components into modern analytical systems. Advancements in electronics, computer science, and robotics drive the evolution of automated sensing systems, overcoming traditional limitations in manual data collection. Environmental sensor networks (ESNs) address challenges in weather constraints and cost considerations, providing high-quality time-series data, although issues in interoperability, calibration, communication, and longevity persist. Unmanned Aerial Systems (UASs), particularly unmanned aerial vehicles (UAVs), play an important role in environmental monitoring due to their versatility and cost-effectiveness. Despite challenges in regulatory compliance and technical limitations, UAVs offer detailed spatial and temporal information. Pollution monitoring faces challenges related to high costs and maintenance requirements, prompting the exploration of cost-efficient alternatives. Smart agriculture encounters hurdle in data integration, interoperability, device durability in adverse weather conditions, and cybersecurity threats, necessitating privacy-preserving techniques and federated learning approaches. Financial barriers, including hardware costs and ongoing maintenance, impede the widespread adoption of smart technology in agriculture. Integrating robotics, notably underwater vehicles, proves indispensable in various environmental monitoring applications, providing accurate data in challenging conditions. This review details the significant role of transfer learning and edge computing, which are integral components of robotics and wireless monitoring frameworks. These advancements aid in overcoming challenges in environmental sensing, underscoring the ongoing necessity for research and innovation to enhance monitoring solutions. Some state-of-the-art frameworks and datasets are analyzed to provide a comprehensive review on the basic steps involved in the automation of environmental sensing applications.

Blockchain and Cryptocurrency Security: Challenges, Innovations, and Future Directions

Blockchain technology and cryptocurrencies have emerged as transformative innovations with the potential to revolutionize various sectors, including finance, supply chain management, and healthcare. However, their widespread adoption also brings forth significant security challenges. This paper provides an overview of the security issues facing blockchain and cryptocurrency systems, explores current research efforts to address these challenges, and discusses potential future directions in blockchain and cryptocurrency security research. We examine topics such as secure consensus mechanisms, smart contract vulnerabilities, privacy-preserving techniques, and regulatory considerations. By understanding these challenges and exploring innovative solutions, we aim to foster the development of more robust and secure blockchain-based systems.

Exploring Threats, Defenses, and Privacy-Preserving Techniques in Federated Learning: A Survey

Exploring Threats, Defenses, and Privacy-Preserving Techniques in Federated Learning: A Survey

Machine Learning Based Theft Detection Using Yolo Object Detection

Abstract: The escalating need for advanced security measures has spurred innovation in the field of computer vision and machine learning. This research project is dedicated to the development of a cutting-edge theft detection system, leveraging machine learning techniques and object detection algorithms. With a focus on enhancing security in surveillance systems, the project addresses the critical challenge of real-time theft detection in various environments. The core of this project involves the exploration and implementation of state-of-the-art machine learning models, including the YOLO (You Only Look Once) algorithm, to detect theft and suspicious activities. An extensive literature survey is conducted to reviewed the research methodology encompasses data collection, preprocessing, model selection, and rigorous evaluation. Diverse datasets are curated, annotated, and used to train and fine-tune the machine learning models. Model performance is assessed using a range of evaluation metrics, including precision, recall, F1 score, and real-time processing capabilities. The project also addresses practical challenges, such as privacy concerns and adaptation to varying environmental conditions. Privacy-preserving techniques, ethical considerations, and adaptability to diverse theft scenarios are integral components of the research. Existing methodologies, benchmark datasets and privacy considerations in the context of theft detection.

Advancements in Privacy-Preserving Techniques for Federated Learning: A Machine Learning Perspective

Federated learning has emerged as a promising paradigm for collaborative machine learning while preserving data privacy. However, concerns about data privacy remain significant, particularly in scenarios where sensitive information is involved. This paper reviews recent advancements in privacy-preserving techniques for federated learning from a machine learning perspective. It categorizes and analyses state-of-the-art approaches within a unified framework, highlighting their strengths, limitations, and potential applications. By providing insights into the landscape of privacy-preserving federated learning, this review aims to guide researchers and practitioners in developing robust and privacy-conscious machine learning solutions for collaborative environments. The paper concludes with future research directions to address ongoing challenges and further enhance the effectiveness and scalability of privacy-preserving federated learning.

MERGE: Fast Private Text Generation

The drastic increase in language models' parameters has led to a new trend of deploying models in cloud servers, raising growing concerns about private inference for Transformer-based models. Existing two-party privacy-preserving techniques, however, only take into account natural language understanding (NLU) scenarios. Private inference in natural language generation (NLG), crucial for applications like translation and code completion, remains underexplored. In addition, previous privacy-preserving techniques suffer from convergence issues during model training and exhibit poor inference speed when used with NLG models due to the neglect of time-consuming operations in auto-regressive generations. To address these issues, we propose a fast private text generation framework for Transformer-based language models, namely MERGE. MERGE reuses the output hidden state as the word embedding to bypass the embedding computation and reorganize the linear operations in the Transformer module to accelerate the forward procedure. Extensive experiments show that MERGE achieves a 26.5x speedup to the vanilla encrypted model under the sequence length 512, and reduces 80% communication cost, with an up to 10x speedup to state-of-the-art approximated models.

Federated learning with blockchain-based model aggregation and incentives

Federated learning is a privacy-preserving machine learning technique that allows mutually distrusting parties to collaboratively train a model without sharing their data. Most federated learning techniques require a centralized aggregator that stores and aggregates models received from multiple parties. However, having a centralized entity may lead to a single point of failure problem. Another problem of federated learning is the leakage of sensitive data through model updates. To address these issues, we propose a Blockchain-based protocol for federated learning. Our protocol uses Blockchain as a model aggregator solving single-point-of-failure problems. Also, we use a Blockchain-based privacy-preserving technique to avoid data leakage problems. We also incorporate a Blockchain-based incentive distribution module to distribute incentives to model contributors. We perform experiments with well-known datasets and show that the proposed model's accuracy is close to that of a centralized aggregator. We also show the overhead of Blockchain by implementing the protocol and running it on Ethereum Blockchain.

Application of Distributed Graphs for Facilitation of Scalable Botnet Detection and Response

Botnets pose a significant threat to modern network environments, exploiting compromised devices to carry out malicious activities such as distributed denial-of-service attacks, spam campaigns, and data theft. Traditional centralized detection systems often struggle to handle the scale and complexity of botnet attacks, leading to delays in detection and response. In response to these challenges, this paper explores the application of distributed graphs for facilitating scalable botnet detection and response. Distributed graphs offer a promising approach for modelling and analyzing complex network structures, enabling efficient detection of botnet propagation patterns and anomalous behaviour across distributed computing environments. The paper presents an overview of distributed graph-based botnet detection systems, discussing their architecture, design considerations, and key concepts such as graph partitioning, vertex-centric computation, and message passing in distributed graph algorithms. Case studies illustrate the practical application of distributed graph-based botnet detection in diverse network environments, highlighting success stories, challenges encountered, and lessons learned from deploying distributed graph systems in production cybersecurity operations. Finally, the paper discusses challenges and open research questions in the field of distributed graph-based botnet detection, addressing issues such as graph partitioning strategies, fault tolerance, privacy-preserving techniques, and integration with other security tools. It proposes potential avenues for future research and development in scalable botnet detection using distributed graphs, emphasizing the importance of adaptive threat response, collaboration with industry partners, and continuous improvement in detection algorithms for enhancing cybersecurity resilience against botnet attacks.

A separable privacy-preserving technique based on reversible medical data hiding in plaintext encrypted images using neural network

A separable privacy-preserving technique based on reversible medical data hiding in plaintext encrypted images using neural network

Preserving fairness and diagnostic accuracy in private large-scale AI models for medical imaging

BackgroundArtificial intelligence (AI) models are increasingly used in the medical domain. However, as medical data is highly sensitive, special precautions to ensure its protection are required. The gold standard for privacy preservation is the introduction of differential privacy (DP) to model training. Prior work indicates that DP has negative implications on model accuracy and fairness, which are unacceptable in medicine and represent a main barrier to the widespread use of privacy-preserving techniques. In this work, we evaluated the effect of privacy-preserving training of AI models regarding accuracy and fairness compared to non-private training.MethodsWe used two datasets: (1) A large dataset (N = 193,311) of high quality clinical chest radiographs, and (2) a dataset (N = 1625) of 3D abdominal computed tomography (CT) images, with the task of classifying the presence of pancreatic ductal adenocarcinoma (PDAC). Both were retrospectively collected and manually labeled by experienced radiologists. We then compared non-private deep convolutional neural networks (CNNs) and privacy-preserving (DP) models with respect to privacy-utility trade-offs measured as area under the receiver operating characteristic curve (AUROC), and privacy-fairness trade-offs, measured as Pearson’s r or Statistical Parity Difference.ResultsWe find that, while the privacy-preserving training yields lower accuracy, it largely does not amplify discrimination against age, sex or co-morbidity. However, we find an indication that difficult diagnoses and subgroups suffer stronger performance hits in private training.ConclusionsOur study shows that – under the challenging realistic circumstances of a real-life clinical dataset – the privacy-preserving training of diagnostic deep learning models is possible with excellent diagnostic accuracy and fairness.

Ensuring Fairness and Gradient Privacy in Personalized Heterogeneous Federated Learning

With the increasing tension between conflicting requirements of the availability of large amounts of data for effective machine learning based analysis, and for ensuring their privacy, the paradigm of federated learning has emerged, a distributed machine learning setting where the clients provide only the machine learning model updates to the server rather than the actual data for decision making. However, the distributed nature of federated learning raises specific challenges related to fairness in a heterogeneous setting. This motivates the focus of our paper, on the heterogeneity of client devices having different computational capabilities and their impact on fairness in federated learning. Furthermore, our aim is to achieve fairness in heterogeneity while ensuring privacy. As far as we are aware there are no existing works that address all these three aspects of fairness, device heterogeneity and privacy simultaneously in federated learning. In this paper, we propose a novel federated learning algorithm with personalization in the context of heterogeneous devices while maintaining compatibility with the gradient privacy preservation techniques of secure aggregation. We analyze the proposed federated learning algorithm under different environments with different datasets, and show that it achieves performance close to or greater than the state-of-the-art in heterogeneous device personalized federated learning. We also provide theoretical proofs for the fairness and convergence properties of our proposed algorithm.

Multimodal biometric user authentication using improved decentralized fuzzy vault scheme based on Blockchain network

Biometric security has emerged as a major concern in the field of Biometric Systems. The existing centralized Biometric Cryptosystems approach still suffers from privacy issues and network-based attacks. In particular, the Biometric Cryptosystems are susceptible to Attack via Record Multiplicity, brute force and collusion attacks. To address these issues, this paper proposes a multimodal biometric user authentication scheme using a decentralized fuzzy vault based on Blockchain technology. In this scheme, the facial biometric images and dorsal hand images together form the multimodal biometric. The proposed security framework is implemented in two phases. In the first phase, multimodal biometric user authentication using an improved fuzzy vault scheme is implemented. In the second phase, a privacy preservation technique using a decentralized fuzzy vault biometric system based on the Blockchain network is implemented. The performance of the proposed framework is verified on the biometric datasets consisting of both genuine and imposter users, and a comparative analysis with the existing state-of-the-art approaches is also presented. The Detection Error Trade-off (DET) curve and the Receiver Operating Characteristic (ROC) curve, obtained for verifying the efficacy of the proposed framework, are also presented. The proposed framework yielded an accuracy of 99.8% and an Equal Error Rate of 0.08. Furthermore, to validate the biometric template protection, the security analysis of the proposed scheme based on irreversibility, unlinkability, and revocability criteria is described. The proposed framework achieves significant improvement in terms of a low False Acceptance Rate (0.08), and a high True Acceptance Rate (0.96).

Quantum-based privacy-preserving techniques for secure and trustworthy internet of medical things an extensive analysis

The advent of Quantum Key Distribution (QKD) and quantum-based privacy-preserving techniques has ushered in a new era for securing communication channels within the Internet of Medical Things (IoMT) systems. We examine the fundamentals, uses, and ramifications of quantum cryptography in relation to healthcare data security in this thorough investigation. The journey commences with an in-depth overview of quantum cryptography, unraveling the concepts of superposition, entanglement, and quantum gates that form the bedrock of quantum computing. As we proceed, we investigate the uses of quantum cryptography, highlighting its contribution to resolving the particular issues brought about by the data-intensive and networked character of IoMT systems. The relevance of QKD in ensuring secure communication within IoMT is meticulously dissected, with case studies and experiments demonstrating the practicality and effectiveness of quantum-based privacy-preserving techniques. From telemedicine networks to wearable health devices, each case study offers valuable insights into the application of quantum-safe cryptography, showcasing its ability to fortify data integrity and confidentiality. A meticulous survey of existing research, coupled with an analysis of advancements in quantum cryptography, provides a panoramic view of the current landscape. From hardware limitations to distance constraints, the study navigates through challenges and breakthroughs, offering a roadmap for the future integration of quantum technologies into IoMT systems.Comparing quantum-based approaches with classical cryptography unveils a nuanced landscape of computational complexity, key distribution methodologies, and real-time encryption considerations. This comparative analysis serves as a guide for healthcare practitioners and technologists in making informed decisions regarding the adoption of quantum-based privacy-preserving techniques in IoMT environments. The case studies and experiments collectively paint a picture of the practicality and promise of quantum-based privacy-preserving techniques in IoMT scenarios. Anticipating future advancements, the exploration extends into quantum hardware improvements, standardization efforts, and the integration of quantum technologies with emerging trends like edge computing and blockchain. As the healthcare industry stands on the cusp of a quantum revolution, the comprehensive insights provided herein offer a foundation for understanding, implementing, and shaping the future of secure healthcare communication through the lens of quantum cryptography.

Enhancing Collaborative Intelligence: Synergistic Approaches for Distributed Machine Learning and Ai Collaboration in Heterogeneous Environments

Collaborative intelligence is vital in distributed machine learning and AI collaboration, especially in heterogeneous environments. This paper explores synergistic approaches to enhance collaborative intelligence by addressing challenges in communication, privacy, resource optimization, domain adaptation, and scalability. The paper reviews existing techniques and methodologies in the field of collaborative intelligence. It discusses protocols, coordination strategies, and communication mechanisms for effective collaboration. Privacy-preserving techniques, such as federated learning and secure multi-party computation, are examined. Resource optimization techniques, including load balancing and adaptive resource allocation, are explored. Domain adaptation and transfer learning methods are also discussed.

Research Advanced in Federated Learning

With the vigorous development of big data, cloud computing and other fields, it has become a global trend to pay attention to data security and privacy. In order to protect their own data security and privacy, different groups are unwilling to contribute their own data information, making the problem of data islands gradually prominent, which seriously restricts the further development of data-driven artificial intelligence. In order to alleviate the above problems, federated learning has attracted more researchers' attention in recent years. Federated Learning is a collaborative decentralized privacy-preserving technique that makes local data available to multiple parties, which not only can the private data be effectively used to train the model but also the leakage of private data can be avoided. Federated learning has been widely used in practical fields such as the financial industry and the Internet of Things industry. This paper systematically introduces the results of research in the field of federated learning in recent years. Specifically, three structures of federated learning are first introduced, and the differences between these three structures are introduced. Then, the most used datasets in training and validation stage were introduced and the shortcoming of each method were introduced to help advanced understanding of FL. Finally, several unsolved problems were introduced and the future prospects in federated learning domain were proposed.

Privacy preservation using optimized Federated Learning: A critical survey

FL is a futuristic research topic that enables cross-sectoral training in ML systems in various organizations with some privacy restrictions. This review article establishes the extensive review of FL with different privacy-preserving techniques and the obstacles involved in the existing privacy-preserving model. This review is initiated by providing the background of FL and provides an overview of the technical details of the component involved in FL. Then it provides a brief review of the around 75 articles related to privacy-preserving in the FL-enabled techniques. Compared to the other survey articles this presented review article provides a brief analysis of the different privacy terms utilized in FL. The categorization of the privacy preservation models in FL highlights the significance of the model and the obstacles that limit the application of the particular privacy preservation model in real-time application. Further, this review articles ensure the details about the year of publishing, performance metrics analyzed in different articles along with their achievements. The limitation experienced in each category of the privacy-preserving technique is elaborated briefly, which assists future researchers to explore more privacy-preserving models in FL.