Anonymization Techniques Research Articles

Optimizing Privacy in Set-Valued Data: Comparing Certainty Penalty and Information Gain

The increase in set-valued data such as transaction records and medical histories has introduced new challenges in data anonymization. Traditional anonymization techniques targeting structured microdata comprising single-attribute- rather than set-valued records are often insufficient to ensure privacy protection in complex datasets, particularly when re-identification attacks leverage partial background knowledge. To address these limitations, this study proposed the Local Generalization and Reallocation (LGR) + algorithm to replace the Normalized Certainty Penalty loss measure (hereafter, NCP) used in traditional LGR algorithms with the Information Gain Heuristic metric (hereafter, IGH). IGH, an entropy-based metric, evaluates information loss based on uncertainty and provides users with the advantage of balancing privacy protection and data utility. For instance, when IGH causes greater information-scale data annotation loss than NCP, it ensures stronger privacy protection for datasets that contain sensitive or high-risk information. Conversely, when IGH induces less information loss, it provides better data utility for less sensitive or low-risk datasets. The experimental results based on using the BMS-WebView-2 and BMS-POS datasets showed that the IGH-based LGR + algorithm caused up to 100 times greater information loss than NCP, indicating significantly improved privacy protection. Although the opposite case also exists, the use of IGH introduces the issue of increased computational complexity. Future research will focus on optimizing efficiency through parallel processing and sampling techniques. Ultimately, LGR+ provides the only viable solution for improving the balance between data utility and privacy protection, particularly in scenarios that prioritize strong privacy or utility guarantees.

Anonymize or synthesize? Privacy-preserving methods for heart failure score analytics

Abstract Aims Data availability remains a critical challenge in modern, data-driven medical research. Due to the sensitive nature of patient health records, they are rightfully subject to stringent privacy protection measures. One way to overcome these restrictions is to preserve patient privacy by using anonymization and synthetization strategies. In this work, we investigate the effectiveness of these methods for protecting patient privacy using real-world cardiology health records. Methods and results We implemented anonymization and synthetization techniques for a structure data set, which was collected during the HiGHmed Use Case Cardiology study. We employed the data anonymization tool ARX and the data synthetization framework ASyH individually and in combination. We evaluated the utility and shortcomings of the different approaches by statistical analyses and privacy risk assessments. Data utility was assessed by computing two heart failure risk scores on the protected data sets. We observed only minimal deviations to scores from the original data set. Additionally, we performed a re-identification risk analysis and found only minor residual risks for common types of privacy threats. Conclusion We could demonstrate that anonymization and synthetization methods protect privacy while retaining data utility for heart failure risk assessment. Both approaches and a combination thereof introduce only minimal deviations from the original data set over all features. While data synthesis techniques produce any number of new records, data anonymization techniques offer more formal privacy guarantees. Consequently, data synthesis on anonymized data further enhances privacy protection with little impacting data utility. We share all generated data sets with the scientific community through a use and access agreement.

Advancements in health information technology and their role in enhancing cancer care: Innovations in early detection, treatment, and data privacy

The rapid advancement of Health Information Technology (HIT) has revolutionized cancer care, introducing new capabilities in early detection, individualized treatment, and data security. This paper reviews the key HIT innovations that have reshaped oncology, including the widespread adoption of Electronic Health Records (EHRs), the use of artificial intelligence (AI) in diagnostic processes, and the application of telemedicine and predictive analytics to improve treatment planning. These technologies have enabled earlier and more accurate diagnoses, expanded access to care, and facilitated data-driven, personalized treatment strategies. In parallel, this review addresses the importance of safeguarding sensitive patient data, focusing on encryption, anonymization techniques, and the regulatory frameworks like HIPAA and GDPR that ensure data privacy. Finally, the paper discusses future directions for HIT, particularly the potential of AI-driven decision support systems, real-time data analytics, and privacy-enhancing algorithms to further advance cancer care, creating a more secure, efficient, and patient-centered healthcare system.

Augmenting Data Privacy Protocols and Enacting Regulatory Frameworks for Cryptocurrencies via Advanced Blockchain Methodologies and Artificial Intelligence

This study examines the effectiveness of current data privacy protocols within cryptocurrency platforms, focusing on encryption strength, anonymity techniques, and AI-powered regulatory compliance tools. Data were sourced from CoinMarketCap and Kaggle, including metrics like Bit Strength, Breach Incidents, and Anonymity Scores, which were analyzed using descriptive statistics, t-tests, and logistic regression. Results showed no significant relationship between encryption strength and breach incidents (p = 0.817), indicating that encryption strength may not be a primary factor in breach prevention. The weak correlation between encryption strength and breaches suggests that other elements, such as platform vulnerabilities or user behaviour, could play a more critical role in security. AI systems, evaluated through metrics like precision (0.168), recall (0.204), and F1 score (0.184), struggled with false positives, showing limitations in accurately detecting breaches and highlighting the need for more refined AI models. Advanced blockchain technologies like Zero-Knowledge Proofs and Homomorphic Encryption enhanced privacy but increased computational costs. It is recommended that hybrid encryption methods be adopted to balance privacy and performance and improve AI systems for more accurate breach detection. Governments must create clear regulations that encourage innovation while ensuring compliance.

Issues in the Spatial Analysis of Police Use of Force Data

This article discusses challenges encountered in the area of spatial analysis of policing practices. Some of the topics discussed include the overlapping of policing jurisdictions and its subsequent effect on the collection and analysis of spatial data, data anonymization techniques that protect individual’s privacy, relationships between use of force outcomes and socioeconomic measures, spatial data sources and collection, data request avenues, the ethics of proprietary spatial software, and the need for open-source spatial analysis software and oversight committees.

Are continuous stop-and-go mixnets provably secure?

This work formally analyzes the anonymity guarantees of continuous stop-and-go mixnets and attempts to answer the titular question. Existing mixnet based anonymous communication protocols that aim to provide provable anonymity guarantees rely on round-based communication models, which requires synchronization among all the nodes and clients that is difficult to achieve in practice. Continuous stop-and-go mixnets (e.g., Loopix and Nym) provide a nice alternative by adding a random delay for each message on every hop independent of all other hops and all other messages. The core anonymization technique of continuous mixnets combined with the fact that the messages are sent by the clients to the mixnet at different times makes it a difficult problem to formally prove security for such mixnet protocols; existing end-to-end analyses for such designs provide only experimental evaluations for anonymity and were lacking a comprehensive formal treatment. We are the first to close that gap and provide a formal analysis. We provide two indistinguishability based definitions (of sender anonymity), namely pairwise unlinkability and user unlinkability, tuned specifically for continuous stop-and-go mixnets. We derive the adversarial advantage as a function of the protocol parameters for the two definitions. We show that there is a fundamental lower bound on the adversarial advantage $\delta$ for pairwise unlinkability; however, strong user unlinkability (negligible adversarial advantage) can be achieved if the users message rate ($\lambda_u$) is proportional to message processing rate ($\lambda$) on the nodes.

Investigating the Perception of Facial Anonymization Techniques in 360° Videos

In this work, we investigate facial anonymization techniques in 360° videos and assess their influence on the perceived realism, anonymization effect, and presence of participants. In comparison to traditional footage, 360° videos can convey engaging, immersive experiences that accurately represent the atmosphere of real-world locations. As the entire environment is captured simultaneously, it is necessary to anonymize the faces of bystanders in recordings of public spaces. Since this alters the video content, the perceived realism and immersion could be reduced. To understand these effects, we compare non-anonymized and anonymized 360° videos using blurring, black boxes, and face-swapping shown either on a regular screen or in a head-mounted display (HMD). Our results indicate significant differences in the perception of the anonymization techniques. We find that face-swapping is most realistic and least disruptive, however, participants raised concerns regarding the effectiveness of the anonymization. Furthermore, we observe that presence is affected by facial anonymization in HMD condition. Overall, the results underscore the need for facial anonymization techniques that balance both photo-realism and a sense of privacy.

Anonymization: The imperfect science of using data while preserving privacy.

Information about us, our actions, and our preferences is created at scale through surveys or scientific studies or as a result of our interaction with digital devices such as smartphones and fitness trackers. The ability to safely share and analyze such data is key for scientific and societal progress. Anonymization is considered by scientists and policy-makers as one of the main ways to share data while minimizing privacy risks. In this review, we offer a pragmatic perspective on the modern literature on privacy attacks and anonymization techniques. We discuss traditional de-identification techniques and their strong limitations in the age of big data. We then turn our attention to modern approaches to share anonymous aggregate data, such as data query systems, synthetic data, and differential privacy. We find that, although no perfect solution exists, applying modern techniques while auditing their guarantees against attacks is the best approach to safely use and share data today.

A comparative Study on AI-Driven Anonymization Techniques for Protecting Personal Data

In the Artificial Intelligence era, protecting individual users' data has become crucial. The collected data is stored in multiple databases having personally identifiable information (PII). This may provide a significant privacy concern for the database. Several privacy-preserving approaches have been proposed, including Differential Privacy, Homomorphic Encryption, Generative Adversarial Network and Federated Learning. In this paper, the above four anonymization techniques are compared. In addition, this study will review the strengths and weaknesses of these techniques. We also discuss the trade-off between data utility and privacy. The results of this study aim to guide researchers and practitioners in selecting suitable AI-driven anonymization techniques.

New Media User Privacy Protection Mechanism Based on Differential Privacy and Data Anonymization

With the rapid development of new media, user privacy issues have become increasingly important. New media platforms, such as TikTok, Twitter, and WhatsApp, process vast amounts of user data daily, including personal information, behavioral data, and social relationships. The extensive collection and use of this data present numerous privacy protection challenges. Many users are not fully aware of how their data is collected and used when using new media platforms, lacking informed consent regarding data collection. Consequently, relying on users to take proactive privacy measures to prevent the disclosure of critical information is difficult to achieve. To address this issue, this paper proposes a privacy protection mechanism that combines differential privacy and data anonymization. The mechanism protects query results through differential privacy techniques and hides user identity information using data anonymization techniques, thereby ensuring user privacy during data analysis and processing. This paper conducts necessary experimental analysis on the proposed method, and the results demonstrate its usability and effectiveness.

Privacy in manifolds: Combining k-anonymity with differential privacy on Fréchet means

While anonymization techniques have improved greatly in allowing data to be used again, it is still really hard to get useful information from anonymized data without risking people’s privacy. Conventional approaches such as k-Anonymity and Differential Privacy have limitations in preserving data utility and privacy simultaneously, particularly in high-dimensional spaces with manifold structures. We address this challenge by focusing on anonymizing data existing within high-dimensional spaces possessing manifold structures. To tackle these issues, we propose and implement a hybrid anonymization scheme termed as the (β, k, b)-anonymization method that combines elements of both differential privacy and k-anonymity. This approach aims to produce high-quality anonymized data that closely resembles real data in terms of knowledge extraction while safeguarding privacy. The Fréchet mean, an operation applicable in metric spaces and meaningful in the manifold setting, serves as a key aspect of our approach. It provides insight into the geometry of data points within high-dimensional spaces. Our goal is to anonymize this Fréchet mean using our proposed approach and minimize the distance between the original and anonymized Fréchet mean to achieve data privacy without significant loss of information. Additionally, we introduce a novel Fréchet mean clustering model designed to enhance the clustering process for high-dimensional spaces. Through theoretical analysis and practical experiments, we demonstrate that our approach outperforms traditional privacy models both in terms of preserving data utility and privacy. This research contributes to advancing privacy-preserving techniques for complex and non-linear data structures, ensuring a balance between data utility and privacy protection.

I still know it's you! On Challenges in Anonymizing Source Code

The source code of a program not only defines its semantics but also contains subtle clues that can identify its author. Several studies have shown that these clues can be automatically extracted using machine learning and allow for determining a program's author among hundreds of programmers. This attribution poses a significant threat to developers of anti-censorship and privacy-enhancing technologies, as they become identifiable and may be prosecuted. An ideal protection from this threat would be the anonymization of source code. However, neither theoretical nor practical principles of such an anonymization have been explored so far. In this paper, we tackle this problem and develop a framework for reasoning about code anonymization. We prove that the task of generating a 𝑘-anonymous program—a program that cannot be attributed to one of 𝑘 authors—is not computable in the general case. As a remedy, we introduce a relaxed concept called 𝑘-uncertainty, which enables us to measure the protection of developers. Based on this concept, we empirically study candidate techniques for anonymization, such as code normalization, coding style imitation, and code obfuscation. We find that none of the techniques provides sufficient protection when the attacker is aware of the anonymization. While we observe a notable reduction in attribution performance on real-world code, a reliable protection is not achieved for all developers. We conclude that code anonymization is a hard problem that requires further attention from the research community.

Strengthening GIS Security: Anonymization and Differential Privacy for Safeguarding Sensitive Geospatial Data

The protection of Geographic Information Systems (GIS) is now more relevant since these systems gather, process, and store geospatial data to various ends, receiving and processing a broad array of applications. Data in the GIS framework is open to everyone, and digital assault, cyber theft, and many more issues which make privacy important. This paper addresses two methods: anonymization and differential privacy to protect GIS data. The performance of anonymization techniques like k-anonymity and geo-indistinguishability and the ability of differential privacy techniques to prevent the reverse engineering of the original data in large datasets are assessed. An area of interest to the research is the applicability of these techniques in reducing the threat of traditional GIS security threats. The paper uses several cases and quantitative evaluation of the results to describe the advantages and disadvantages of both types of analysis and to demonstrate how these analyses can be applied in practice. These methods show that data breaches are minimized and general data protection improved by as much as 30% for location-specific attacks, for instance. This research seeks to address the application of privacy-preserving techniques in the GIS while requiring high privacy standards in using geospatial datasets. Importantly, the study's findings are intended to inform policymakers and system designers of the best practices for improving GIS security structures.

UMAP-SMOTENC: A simple, efficient, and consistent alternative for privacy-aware synthetic data generation

The intensification of governmental legislation and the social awareness around data privacy protection severely constrains organizations' data utilization capabilities. As a result, the interest in data anonymization techniques, which should preserve the patterns present in the original data but mitigate the risks of privacy leakage, has also increased. While conventional methods may compromise privacy, recently proposed deep learning generative approaches are computationally expensive and unreliable when used in tabular datasets, hindering the democratization and usability of data. In this paper, we explore this trade-off between privacy and the quality of the anonymized data, establishing a new equilibrium obtained using a synthetic oversampling technique, SMOTE-NC, on a non-linear compressed version of the input space, achieved with the application of UMAP. The introduced approach, UMAP-SMOTENC, constitutes an efficient and consistent solution that can be used without significant efforts on hyperparameter tuning or resourcing to massive computing infrastructures. An experiment was conducted to evaluate the robustness of the proposed solution, comparing several metrics and models across eight datasets with diverse characteristics. The results achieved suggest that the presented method can efficiently synthesize privacy-aware data while conserving the relevant patterns of the real dataset, particularly those required for classification tasks.

AI-Driven Privacy in Elderly Care: Developing a Comprehensive Solution for Camera-Based Monitoring of Older Adults

The need for privacy in elderly care is crucial, especially where constant monitoring can intrude on personal dignity. This research introduces the development of a unique camera-based monitoring system designed to address the dual objectives of elderly care: privacy and safety. At its core, the system employs an AI-driven technique for real-time subject anonymization. Unlike traditional methods such as pixelization or blurring, our proposed approach effectively removes the subject under monitoring from the scene, replacing them with a two-dimensional avatar. This is achieved through the use of YOLOv8, which facilitates accurate real-time person detection and pose estimation. Furthermore, the proposed system incorporates a fall detection algorithm that utilizes a residual causal convolutional network together with motion features of persons to identify emergency situations and promptly notify caregivers in the event of a fall. The effectiveness of the system is evaluated to emphasize its advanced privacy protection technique and fall detection capabilities using several metrics. This evaluation demonstrates the system’s proficiency in real-world applications and its potential to enhance both safety and privacy in elderly care environments.

XimSwap: Many-to-Many Face Swapping for TinyML

The unprecedented development of deep learning approaches for video processing has caused growing privacy concerns. To ensure data analysis while maintaining privacy, it is essential to address how to protect individuals’ identities. One solution is to anonymize data at the source, avoiding the transmission or storage of information that could lead to identification. This study introduces XimSwap, a novel deep learning technique for real-time video anonymization, which can remove facial identification features directly on edge devices with minimal computational resources. Our approach offers a comprehensive solution that guarantees privacy by design. This novel method for implementing face-swapping ensures that the pose and expression of a target face remain unchanged and can be used on embedded devices with very limited computational resources. By incorporating style transfer layers into convolutional ones and optimizing the network’s operation, we achieved a reduction of over 98% in the required operations and parameters compared with state-of-the-art architectures. Our approach also significantly reduces RAM usage, making it possible to implement the anonymization process on tiny edge devices, including microcontrollers, such as the STM32H743.

The Anonymous Data Warehouse: A Hands-On Framework for Anonymizing Data From Digital Health Applications.

The digital health space is growing rapidly, and so is the interest in sharing anonymized health data. However, data anonymization techniques have yet to see much coverage in the medical literature. The purpose of this article is, therefore, toprovide a practical framework for anonymization with a focus on the unique properties of data from digital health applications.Literature trends, as well as common anonymization techniques, were synthesized into a framework that considers the opportunities and challenges of digital health data. A rationale for each design decision is provided, and the advantages and disadvantages are discussed. We propose a framework based on storing data separately, anonymizing the data where the identified data is located, only exporting selected data, minimizing static attributes, ensuring k-anonymity of users and their static attributes, and preventing defined metrics from acting as quasi-identifiers by using aggregation, rounding, and capping. Data anonymization requires a pragmatic approach that preserves the utility of the data while minimizing reidentification risk. The proposed framework should be modified according to the characteristics of the respective data set.

A comprehensive survey of performance, security and privacy issues in the network interface layer of the TCP/IP

The network interface layer of the TCP/IP protocol suite, primarily comprised of the Internet Protocol (IP), serves as the backbone of modern internet communication. With its efficient data delivery, The network interface layer, presents key challenges in terms of performance, security, and privacy. This comprehensive survey delves into these three crucial aspects, analyzing the inherent vulnerabilities, limitations of the interface layer, and provide solutions of the related problems. The performance analysis explores throughput, latency, and bandwidth constraints, along with solutions such as bandwidth allocation and optimization techniques. Vulnerabilities within Network Interface Layer, including denial-of-service attacks and MAC address spoofing, are discussed, along with a review of existing security mechanisms. Privacy flaws are examined, covering MAC address tracking, profiling risks, and anonymization techniques, while also addressing privacy considerations on the Internet of Things. The survey analyzes several case studies providing comparative analysis of the network interface layer protocols, with support of the real world scenarios including performance analysis in high density environment, and security and privacy risks in smart homes networks. The findings provide a comprehensive understanding of the complexities surrounding performance, security, and privacy issues future directions and potential solutions.

Enhancing Data Privacy in Wireless Sensor Networks: Investigating Techniques and Protocols to Protect Privacy of Data Transmitted Over Wireless Sensor Networks in Critical Applications of Healthcare and National Security

The article discusses the emergence of Wireless Sensor Networks (WSNs) as a groundbreaking technology in data processing and communication. It outlines how WSNs, composed of dispersed autonomous sensors, are utilized to monitor physical and environmental factors, transmitting data wirelessly for analysis. The article explores various applications of WSNs in healthcare, national security, emergency response, and infrastructure monitoring, highlighting their roles in enhancing patient care, public health surveillance, border security, disaster management, and military operations. Additionally, it examines the foundational concepts of data privacy in WSNs, focusing on encryption techniques, authentication mechanisms, anonymization techniques, and access control mechanisms. The article also addresses vulnerabilities, threats, and challenges related to data privacy in healthcare and national security contexts, emphasizing regulatory compliance, ethical considerations, and socio-economic factors. Furthermore, it introduces the Diffusion of Innovation Theory as a framework for understanding the adoption of privacy-enhancing technologies in WSNs. Finally, the article reviews empirical studies demonstrating the efficacy of security solutions in preserving data privacy in WSNs, offering insights into advancements in safeguarding sensitive information.

Digital Privacy Rights in the Age of Big Data: Balancing Security and Civil Liberties

In the era of big data, the protection of digital privacy rights has become a paramount concern, requiring a delicate balance between security measures and civil liberties. This article explores the challenges and complexities surrounding digital privacy in the age of big data and examines the strategies and policies aimed at safeguarding individuals' privacy rights. Through qualitative methods such as literature review and library research, this study delves into the multifaceted nature of digital privacy rights and the implications of big data analytics on personal data protection. The literature review reveals the rapid proliferation of digital technologies and the extensive collection, storage, and analysis of personal data by governments, corporations, and other entities. While big data analytics offer numerous benefits, including enhanced security and personalized services, they also raise significant concerns regarding privacy intrusion, surveillance, and data breaches. Key themes explored in this study include the evolution of privacy laws and regulations, the role of encryption and data anonymization techniques in preserving privacy, and the ethical considerations surrounding data usage and consent. Additionally, the article examines the impact of emerging technologies such as artificial intelligence and the Internet of Things on digital privacy rights. Furthermore, the study discusses the importance of transparency, accountability, and user control in ensuring the protection of digital privacy rights. It also highlights the need for collaborative efforts between governments, industry stakeholders, and civil society to develop robust privacy frameworks that uphold both security and civil liberties in the digital age. In conclusion, as the volume and complexity of digital data continue to grow exponentially, safeguarding digital privacy rights remains a pressing challenge. By adopting a comprehensive approach that balances security imperatives with respect for individual freedoms, society can navigate the complexities of the digital landscape while preserving fundamental rights to privacy and autonomy.