Anonymization Methods Research Articles

A NOVEL PRIVACY PRESERVATION OF HEALTHCARE DATA WITH INFORMATION ENTROPY-BASED MULTI-SCHEME FULLY HOMOMORPHIC ENCRYPTION AND RIVEST SHAMIR ADLEMAN TECHNIQUES

Problem background: The biggest problem in the medical field is data security and privacy. Enhanced protection of data is required as a result of the implementation of increasing laws and regulations, the utilization of electronic patient records, the reorganization of providers, and the growing necessitate for data across patients, taxpayers, and physicians. Several standard methods of anonymization are more effective in sanitizing data, yet they are unsuccessful in restoring the data. Although a few privacy preservation algorithms have been established in recent days, the accuracy of maintaining both private and insensitive information seems to be relatively low. Objective: The aim is to develop a novel system for privacy preservation. Here, the encryption process is done using the Information Entropy-based Adaptive Encryption Technique (IEAET). The health data are gathered from appropriate online sources. The sensitive and insensitive attributes are distinguished with the help of Information Entropy. From these data, the sensitive attributes are analyzed by the information entropy optimally selected using the Enhanced Walrus Optimization Algorithm (EWaOA). Then the sensitive attributes are encrypted using the Multi-scheme Fully Homomorphic Encryption (Multi-scheme FHE) approach. Here, the key required for encryption is optimally generated using the proposed EWaOA, and this key is utilized for the encryption of sensitive data with high integrity. Finally, the insensitive attributes are enciphered using the Rivest–Shamir–Adleman (RSA) algorithm. Therefore, the health data are preserved with more security without any information loss. Result: The outcome of the proposed method provided the key variation of restoration efficiency is 98.5%. Discussion: The developed model provides better restoration efficiency by using an optimization algorithm to optimally select the attributes. It provides more effective outcomes and security, and is well performed in healthcare data preservation to enhance the privacy of data. The developed model is used for reducing the risk and saving the cost. Thus, it proved that the developed model significantly outperformed conventional methods and its reliability was also improved.

Comparison of anonymization techniques regarding statistical reproducibility.

Anonymization opens up innovative ways of using secondary data without the requirements of the GDPR, as anonymized data does not affect anymore the privacy of data subjects. Anonymization requires data alteration, and this project aims to compare the ability of such privacy protection methods to maintain reliability and utility of scientific data for secondary research purposes. The French data protection authority (CNIL) defines anonymization as a processing activity that consists of using methods to make impossible any identification of people by any means in an irreversible manner. To answer project's objective, a series of analyses were performed on a cohort, and reproduced on four sets of anonymized data for comparison. Four assessment levels were used to evaluate impact of anonymization: level 1 referred to the replication of statistical outputs, level 2 referred to accuracy of statistical results, level 3 assessed data alteration (using Hellinger distances) and level 4 assessed privacy risks (using WP29 criteria). 87 items were produced on the raw cohort data and then reproduced on each of the four anonymized data. The overall level 1 replication score ranged from 67% to 100% depending on the anonymization solution. The most difficult analyses to replicate were regression models (sub-score ranging from 78% to 100%) and survival analysis (sub-score ranging from 0% to 100. The overall level 2 accuracy score ranged from 22% to 79% depending on the anonymization solution. For level 3, three methods had some variables with different probability distributions (Hellinger distance = 1). For level 4, all methods had reduced the privacy risk of singling out, with relative risk reductions ranging from 41% to 65%. None of the anonymization methods reproduced all outputs and results. A trade-off has to be find between context risk and the usefulness of data to answer the research question.

Evaluation of EGFR-TKIs and ICIs treatment stratification in non-small cell lung cancer using an encrypted multidimensional radiomics approach

BackgroundRadiomics holds great potential for the noninvasive evaluation of EGFR-TKIs and ICIs responses, but data privacy and model robustness challenges limit its current efficacy and safety. This study aims to develop and validate an encrypted multidimensional radiomics approach to enhance the stratification and analysis of therapeutic responses.Materials and methodsThis multicenter study incorporated various data types from 506 NSCLC patients, which underwent preprocessing through anonymization methods and were securely encrypted using the AES-CBC algorithm. We developed one clinical model and three radiomics models based on clinical factors and radiomics scores (RadScore) of three distinct regions to evaluate treatment response. Additionally, an integrated radiomics-clinical model was created by combining clinical factors with RadScore. The study also explored the association between different EGFR mutations and PD-1/PD-L1 expression in radiomics biomarkers.FindingsThe radiomics-clinical model demonstrated high performance, with AUC values as follows: EGFR (0.884), 19Del (0.894), L858R (0.881), T790M (0.900), and PD-1/PD-L1 expression (0.893) in the test set. This model outperformed both clinical and single radiomics models. Decision curve analysis further supported its superior clinical utility. Additionally, our findings suggest that the efficacy of EGFR-TKIs and ICIs therapy may not depend on detecting a singular tumor feature or cell type.ConclusionThe proposed method effectively balances the level of evidence with privacy protection, enhancing the study’s validity and security. Therefore, radiomics biomarkers are expected to complement molecular biology analyses and guide therapeutic strategies for EGFR-TKIs, ICIs, and their combinations.

AI-replicas as ethical practice: introducing an alternative to traditional anonymisation techniques in image-based research

This article introduces the use of AI-replicas as an alternative to traditional anonymisation methods in image-based qualitative research. It emphasises the ethical and practical dilemmas posed by current anonymisation methods, such as distortion or loss of emotional and contextual information in images, and proposes the use of AI-replicas to preserve the integrity and authenticity of visual data while ensuring participant anonymity. The article outlines the technological foundations of generative artificial intelligence (AI) and the practical application of Stable Diffusion to generate AI-replicas for anonymisation and fictionalisation purposes. Furthermore, it discusses the potential biases present in generative AI to suggest ways to mitigate these biases through careful prompt engineering and participatory approaches. The introduced approach aims to enhance ethical practices in visual research by providing a method that ensures participant anonymity without compromising the data's qualitative richness and interpretative validity.

An anonymity communication method for IoT based electronic health record

An anonymity communication method for IoT based electronic health record

Evaluating the Impact of Face Anonymization Methods on Computer Vision Tasks: A Trade-Off Between Privacy and Utility

Evaluating the Impact of Face Anonymization Methods on Computer Vision Tasks: A Trade-Off Between Privacy and Utility

Forensic Anonymous Communication and Fraud Detection in Public Polytechnics in Oyo State, Nigeria

This study explored the impact of forensic anonymous communication on fraud detection in public polytechnics in Oyo state, Nigeria. Data was gathered from both primary and secondary sources using a well-structured closed ended questionnaire. The study employed survey research design. The population of the study comprised 50 staff of the bursary unit of each of the four (4) public polytechnics in Oyo state, Nigeria to make up the total of 200 participants. Findings from the simple percentage analysis revealed that anonymous communications are suitable forensic investigation techniques because they are efficient and transparent; and facts and figures can be gotten within a reasonable period. The study then concluded that Anonymous Communication with the combination of two or more techniques, should be used by forensic accountants, forensic legal practitioners, and all other similar parties so as to have flexibility to test different methods in the practice of forensic accounting and fraud investigation. Therefore, the study recommended that the use of anonymous communication methods should be encouraged as well as extensive awareness should be put in place for the use by the public as well as quick responses to queries should be encouraged; government should demonstrate more interest in forensic accounting for monitoring and pursuing suspected perpetrators in fraud cases which is critical in higher institutions, particularly among bursary staff and management; that forensic accounting techniques should be deployed to help prevent and reduce fraudulent activity.

ВЕЛИКІ МОВНІ МОДЕЛІ ТА ОСОБИСТА ІНФОРМАЦІЯ: ПРОБЛЕМИ БЕЗПЕКИ ТА ШЛЯХИ ЇХ ВИРІШЕННЯ ЗА ДОПОМОГОЮ АНОНІМІЗАЦІЇ

In light of the growing capabilities of Large Language Models (LLMs), there is an urgent need for effective methods to protect personal data in online texts. Existing anonymization methods often prove ineffective against complex LLM analysis algorithms, especially when processing sensitive information such as medical data. This research proposes an innovative approach to anonymization that combines k-anonymity and adversarial methods. Our approach aims to improve the efficiency and speed of anonymization while maintaining a high level of data protection. Experimental results on a dataset of 10,000 comments showed a 40% reduction in processing time (from 250 ms to 150 ms per comment) compared to traditional adversarial methods, a 5% improvement in medical data anonymization accuracy (from 90% to 95%), and a 7% improvement in data utility preservation (from 85% to 92%). Special attention is paid to the application of the method in the context of interaction with LLM-based chatbots and medical information processing. We conduct an experimental evaluation of our method, comparing it with existing industrial anonymizers on real and synthetic datasets. The results demonstrate significant improvements in both data utility preservation and privacy protection. Our method also takes into account GDPR requirements, setting a new standard in the field of data anonymization for AI interactions. This research offers a practical solution for protecting user privacy in the era of LLMs, especially in sensitive areas such as healthcare. Keywords: AI, data security, ML, LLM, privacy.

An Open Source Python Library for Anonymizing Sensitive Data

Open science is a fundamental pillar to promote scientific progress and collaboration, based on the principles of open data, open source and open access. However, the requirements for publishing and sharing open data are in many cases difficult to meet in compliance with strict data protection regulations. Consequently, researchers need to rely on proven methods that allow them to anonymize their data without sharing it with third parties. To this end, this paper presents the implementation of a Python library for the anonymization of sensitive tabular data. This framework provides users with a wide range of anonymization methods that can be applied on the given dataset, including the set of identifiers, quasi-identifiers, generalization hierarchies and allowed level of suppression, along with the sensitive attribute and the level of anonymity required. The library has been implemented following best practices for integration and continuous development, as well as the use of workflows to test code coverage based on unit and functional tests.

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

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. 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. 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.

Anonymization of Voices in Spaces for Civic Dialogue: Measuring Impact on Empathy, Trust, and Feeling Heard

Anonymity is a powerful component of many participatory media platforms that can afford people greater freedom of expression and protection from external coercion and interference. However, it can be difficult to effectively implement on platforms that leverage spoken language due to distinct biomarkers present in the human voice. In this work, we explore the use of voice anonymization methods within the context of a technology-enhanced civic dialogue network based in the United States, whose purpose is to increase feelings of agency and being heard within civic processes. Specifically, we investigate the use of two different speech transformation and synthesis methods for anonymization: voice conversion (VC) and text-to-speech (TTS). Through a series of two studies, we examine the impact that each method has on 1) the empathy and trust that listeners feel towards a person sharing a personal story, and 2) a speaker's own perception of being heard, finding that voice conversion is an especially suitable method for our purposes. Our findings open up interesting potential research directions related to anonymous spoken discourse, as well as additional ways of engaging with voice-based civic technologies.

Enhancing Security in Geographic Information Systems: Anonymization and Differential Privacy Techniques for Protecting Sensitive Geospatial Data

As Geographic Information Systems (GIS) increasingly facilitate the analysis and sharing of geospatial data, the protection of sensitive information becomes paramount. This research explores the implementation of anonymization and differential privacy techniques to enhance security in GIS. Anonymization methods effectively remove or obscure personally identifiable information from geospatial datasets, while differential privacy introduces a mathematical framework that allows for the sharing of aggregate data without compromising individual privacy. This study evaluates the strengths and weaknesses of these techniques, demonstrating their effectiveness in maintaining the utility of geospatial data while safeguarding sensitive information. Through case studies and comparative analysis, we provide insights into best practices for integrating these privacy-preserving strategies into GIS applications, ensuring compliance with legal regulations and fostering public trust in geospatial technologies.

TCα-PIA: A Personalized Social Network Anonymity Scheme via Tree Clustering and α-Partial Isomorphism

Social networks have become integral to daily life, allowing users to connect and share information. The efficient analysis of social networks benefits fields such as epidemiology, information dissemination, marketing, and sentiment analysis. However, the direct publishing of social networks is vulnerable to privacy attacks such as typical 1-neighborhood attacks. This attack can infer the sensitive information of private users using users’ relationships and identities. To defend against these attacks, the k-anonymity scheme is a widely used method for protecting user privacy by ensuring that each user is indistinguishable from at least k−1 other users. However, this approach requires extensive modifications that compromise the utility of the anonymized graph. In addition, it applies uniform privacy protection, ignoring users’ different privacy preferences. To address the above challenges, this paper proposes an anonymity scheme called TCα-PIA (Tree Clustering and α-Partial Isomorphism Anonymization). Specifically, TCα-PIA first constructs a similarity tree to capture subgraph feature information at different levels using a novel clustering method. Then, it extracts the different privacy requirements of each user based on the node cluster. Using the privacy requirements, it employs an α-partial isomorphism-based graph structure anonymization method to achieve personalized privacy requirements for each user. Extensive experiments on four public datasets show that TCα-PIA outperforms other alternatives in balancing graph privacy and utility.

Against linkage: A novel generative face anonymization framework with style diversification

AbstractWith the advent of the digital era, millions of facial images are shared online daily, posing severe privacy threats. Generative face anonymization (GFA) methods generate virtual faces to conceal original identities, protecting sensitive information while preserving utility. However, deep learning based user identity linkage (UIL) methods can link similar faces to the same identity and leverage the linked profiles for malicious purposes, including localization and behaviour prediction. These UIL methods pose a significant challenge to the diversity of virtual faces, a challenge that existing GFA methods have not adequately addressed. To address this research gap, we propose Style Diversification‐based Generative Face Anonymization (SD‐GFA), a framework that generates virtual faces with diverse identities and high visual quality. SD‐GFA features an equalized control module to balance input faces and user‐specified keys, a face generation module with a re‐connection strategy for high‐quality synthesis, and a maximum probability simulation module to enhance diversity. Our experiments demonstrate that SD‐GFA effectively mitigates linkage risk by improving the diversity of virtual faces, while also enhancing their utility and visual quality. This study provides a robust solution to enhance the security of anonymized faces shared on the internet.

A blind flow fingerprinting and correlation method against disturbed anonymous traffic based on pattern reconstruction

A blind flow fingerprinting and correlation method against disturbed anonymous traffic based on pattern reconstruction

A Secure and Efficient Authentication Scheme for Large-Scale IoT Devices Based on Zero-Knowledge Proof

Current authentication schemes based on zero-knowledge proof (ZKP) still face issues such as high computation costs, low efficiency, and security assurance difficulty. Therefore, we propose a secure and efficient authentication scheme (SEAS) for large-scale IoT devices based on ZKP. In the initialization phase, the trusted authority creates prerequisites for device traceability and system security. Then, we propose a new registration method to ensure device anonymity. In the identity tracing and revocation phase, we revoke the real identity of abnormal devices by decrypting and updating group public keys, avoiding their access and reducing revocation costs. In the authentication phase, we check the arithmetic relationship between blind certificates, proofs, and other random data. We propose a new anonymous batch authentication method to effectively reduce computation costs, enhance authentication efficiency, and guarantee device authentication security. Security analysis and experimental results show that an SEAS can ensure security and effectively reduce verification time and energy costs. Its security and performance exceed existing schemes.

Evaluating the disclosure risk of anonymized documents via a machine learning-based re-identification attack

The availability of textual data depicting human-centered features and behaviors is crucial for many data mining and machine learning tasks. However, data containing personal information should be anonymized prior making them available for secondary use. A variety of text anonymization methods have been proposed in the last years, which are standardly evaluated by comparing their outputs with human-based anonymizations. The residual disclosure risk is estimated with the recall metric, which quantifies the proportion of manually annotated re-identifying terms successfully detected by the anonymization algorithm. Nevertheless, recall is not a risk metric, which leads to several drawbacks. First, it requires a unique ground truth, and this does not hold for text anonymization, where several masking choices could be equally valid to prevent re-identification. Second, it relies on human judgements, which are inherently subjective and prone to errors. Finally, the recall metric weights terms uniformly, thereby ignoring the fact that the influence on the disclosure risk of some missed terms may be much larger than of others. To overcome these drawbacks, in this paper we propose a novel method to evaluate the disclosure risk of anonymized texts by means of an automated re-identification attack. We formalize the attack as a multi-class classification task and leverage state-of-the-art neural language models to aggregate the data sources that attackers may use to build the classifier. We illustrate the effectiveness of our method by assessing the disclosure risk of several methods for text anonymization under different attack configurations. Empirical results show substantial privacy risks for most existing anonymization methods.

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.

Analyzing anonymous activities using Interrupt-aware Anonymous User-System Detection Method (IAU-S-DM) in IoT

The intrusion detection process is important in various applications to identify unauthorized Internet of Things (IoT) network access. IoT devices are accessed by intermediators while transmitting the information, which causes security issues. Several intrusion detection systems are developed to identify intruders and unauthorized access in different software applications. Existing systems consume high computation time, making it difficult to identify intruders accurately. This research issue is mitigated by applying the Interrupt-aware Anonymous User-System Detection Method (IAU-S-DM). The method uses concealed service sessions to identify the anonymous interrupts. During this process, the system is trained with the help of different parameters such as origin, session access demands, and legitimate and illegitimate users of various sessions. These parameters help to recognize the intruder’s activities with minimum computation time. In addition, the collected data is processed using the deep recurrent learning approach that identifies service failures and breaches, improving the overall intruder detection rate. The created system uses the TON-IoT dataset information that helps to identify the intruder activities while accessing the different data resources. This method’s consistency is verified using the metrics of service failures of 10.65%, detection precision of 14.63%, detection time of 15.54%, and classification ratio of 20.51%.

Extensive Review of Security and Privacy Issues in Heterogeneous Networks

This paper studies advanced network security, privacy, and performance issues for Heterogeneous Networks (HetNets), which combine multiple types of cells to enhance wireless communication coverage and capacity. The coexistence of heterogeneous network elements inherently leads to considerable issues in terms of security, privacy, and performance. Previous research has primarily addressed security issues using common cryptographic techniques including data encryption with AES, secure key exchange with RSA, and handoff security with mutual authentication. Data anonymization methods and regulatory compliance have been used to address privacy concerns. Allocating resources and managing interference are some of the tactics that have been used in performance optimization. Although the current solutions offer a solid base, they frequently have issues with computational overhead, scalability, and continuous maintenance needs. Performance optimization solutions might not sufficiently handle dynamic network conditions, and privacy protections might not be sufficient to mitigate sophisticated data harvesting operations. This study evaluates existing HetNets solutions using a thorough evaluation and analytical technique. It assesses how well they handle issues with security, privacy, and performance, points out any shortcomings, and suggests areas for further investigation. Our analysis emphasizes the need for improved security mechanisms, including quantum-resistant cryptography, AI-driven threat detection, and technologies that improve privacy, such as differential privacy and homomorphic encryption. Innovative resource management and optimization strategies catered to HetNets' dynamic nature are needed to address performance issues. The necessity of developing new security, privacy, and performance solutions to guarantee the stability and dependability of HetNets is emphasized by this study. Stakeholders can promote the broad adoption and smooth integration of HetNet technology by tackling these obstacles. To sum up, this study offers a thorough analysis of the HetNets' architectural, security, privacy, and performance issues. It lists existing remedies, analyses their drawbacks, and suggests new lines of inquiry for future development to advance the area and improve HetNets' operational capabilities.