Privacy Protection in High Security Biometrics Applications

Despite their usability advantages over traditional authentication systems, biometrics-based authentication systems suffer from inherent privacy violation and non-revocability issues. In order to address these issues, the concept of cancelable biometrics was introduced as a means of generating multiple, revocable, and noninvertible identities from true biometric templates. Apart from BioHashing, which is a two-factor cancelable biometrics technique based on mixing a set of tokenized user-specific random numbers with biometric features, cancelable biometrics techniques usually cannot preserve the recognition accuracy achieved using the unprotected biometric systems. However, as the employed token can be lost, shared, or stolen, BioHashing suffers from the same issues associated with token-based authentication systems. In this paper, a reliable tokenless cancelable biometrics scheme, referred to as BioEncoding, for protecting IrisCodes is presented. Unlike BioHashing, BioEncoding can be used as a one-factor authentication scheme that relies only on sole IrisCodes. A unique noninvertible compact bit-string, referred to as BioCode, is randomly derived from a true IrisCode. Rather than the true IrisCode, the derived BioCode can be used efficiently to verify the user identity without degrading the recognition accuracy obtained using original IrisCodes. Additionally, BioEncoding satisfies all the requirements of the cancelable biometrics construct. The performance of BioEncoding is compared with the performance of BioHashing in the stolen-token scenario and the experimental results show the superiority of the proposed method over BioHashing-based techniques.

The widespread adoption of consumer electronics (CE), particularly in smart home ecosystems, has led to the proliferation of intelligent devices such as smart cameras, video doorbells, and smart door locks. While these devices enhance convenience and automation, they generate massive amounts of visual data containing sensitive personal information, thereby increasing the risk of privacy leakage and challenging traditional encryption methods in consumer IoT networks. To address this issue, a cryptanalysis-driven image encryption scheme via a chaos-based 3D S-box and genetic code is proposed, specifically designed to safeguard visual privacy in consumer applications. First, a novel dynamical architecture, formally termed 3D Non-degenerate Hyperchaos (3D-NDHC), is constructed to serve as a robust entropy source. Explicitly defined as a globally bounded volume-expanding hyperchaotic system, 3D-NDHC distinguishes itself from traditional dissipative maps by exhibiting global robust hyperchaos and scalable complexity, where the Lyapunov exponents grow logarithmically with control parameters, ensuring high unpredictability. Next, a dynamically generated three-dimensional S-box driven by these chaotic sequences is introduced to perform efficient image permutation. Subsequently, a genetic code method enhanced by cryptanalysis is applied to confuse the permuted image matrix, specifically addressing the security vulnerabilities often inherent in lightweight encryption algorithms. Finally, under the control of pseudo-chaotic sequences, a lightweight wave diffusion process is employed to spread the pixel values and produce the final ciphertext image. In contrast to conventional algorithms that rely on standard encryption techniques, the proposed approach integrates a chaos-driven 3D S-box with a cryptanalysis-enhanced genetic code scheme. This combination introduces a high level of confusion and diffusion, significantly increasing the difficulty for attackers attempting to illegally decrypt sensitive images from smart devices. Experimental results and security analyses demonstrate that the scheme effectively resists various cryptographic attacks and outperforms several recently proposed image encryption algorithms, offering a robust solution for protecting digital image privacy in smart home environments.

Although the current face de-recognition algorithm can effectively protect the user’s identity, it will change the original attributes of the image, destroy the visual effect, or change the characteristics of the image to varying degrees. Traditional methods such as pixelation and occlusion can effectively realize face de-recognition, but the processed image cannot be used for basic processing such as face statistics and expression recognition, which greatly destroys the usability of the image in the field of online new media. In order to preserve more attributes of the original image and reduce the changes of facial skin color and texture features caused by the above de-recognition algorithm’s operation on the whole facial area, this paper proposes a privacy protection model of face image based on generative adversarial network and facial feature analysis. Our model realizes face recognition based on changing the original image as little as possible. Specifically, we identify and extract features from fixed parts in face images, including eyes, nose, mouth, eyebrows, and cheeks. Then, we designed a face privacy recognition and protection model based on conditional generative adversarial networks. We verify our proposed method on two most commonly used face datasets in face research, namely CelebFaces Attributes and Labeled Faces in the Wild, and the results show that our proposed method achieve better performance compared with other face privacy protection methods.

Over-Privileged Permissions: Using Technology and Design to Create Legal Compliance

An effective iris biometric privacy protection scheme with renewability

The article examines the implications of big data for competition law, with a focus on personal data and the privacy concerns that such data may give rise to, especially in the area of merger control. Today, one of the biggest challenges for competition authorities in data-driven markets is how to deal with issues related to personal data and the protection of privacy in their analysis. A key question is the role of competition law in protecting consumers from potential data privacy risks arising in the context of mergers in digital markets. The article also engages with one of the currently most debated topics in the competition community, namely the competition-privacy interface, and considers how personal data in the digital economy is considered a currency in exchange for online offerings, and how a loss of privacy can be factored into quality competition. The article addresses some of the challenges with incorporating privacy as a non-price parameter into competition analysis and offers food for thought by discussing relevant methodologies to assign monetary values to personal data.

After Privacy: The Rise of Facebook, the Fall of Wikileaks, and Singapore’s Personal Data Protection Act 2012

Nowadays, biometric recognition represents an integral component of identity management and access control systems, replacing PINs or passwords. However, the wide deployment of biometric recognition systems in the past decades has raised numerous privacy concerns regarding the storage and use of biometric data. Due to the fact that the link between individuals and their biometric characteristics, e.g. fingerprints or iris, is strong and permanent, biometric reference data (templates) need to be protected in order to safeguard individuals' privacy and biometric systems' security. In particular, unprotected biometric templates can be abused to crossmatch biometric databases, i.e. tracking individuals without consent, and to launch presentation attacks employing specific inversion techniques. Technologies of biometric template protection offer solutions to privacy preserving biometric authentication, which improves the public confidence and acceptance of biometric systems. While biometric template protection has been an active research topic over the last 20 years, proposed solutions are still far from gaining practical acceptance. The existing gap between theory and practice, which results in a trade-off between privacy protection and recognition accuracy, is caused by numerous factors. To facilitate a standardized design and application of biometric template protection schemes, researchers have to solve several open problems. This chapter provides an overview of state-of-the-art technologies of biometric template protection and discusses main issues and challenges.

Objective – To examine Serbia's deployment of Chinese-supplied surveillance technologies and assess the implications for democracy, privacy, and civil liberties in the context of Serbia's deteriorating freedom indices and EU accession aspirations. Goal – To analyse the technological cooperation between China and Serbia in surveillance infrastructure, evaluate the transparency and accountability of Serbian institutions in implementing these systems, review the legal framework governing digital surveillance, and assess local and international responses to this deployment. Methodology – This study employs a qualitative research approach based on document analysis from diverse, highly reliable sources. The methodology prioritises relevance, reliability, and diversity, integrating academic publications, reports from established human rights organisations, investigative journalism, and official government documents. The 41 sources were selected to ensure comprehensive coverage of technological, legal, political, and social dimensions of surveillance deployment in Serbia. Findings – The research reveals that Serbia has significantly expanded its state surveillance capabilities through a strategic partnership with China, particularly with Huawei, deploying thousands of cameras equipped with facial and license plate recognition across major cities. The findings demonstrate a troubling lack of transparency and accountability, with agreements classified as confidential and explicit references to Chinese involvement deliberately obscured. Serbia's legal framework for digital surveillance remains underdeveloped, lacking adequate oversight mechanisms and privacy protections. Locally, concerns persist about the political misuse of surveillance for control rather than public safety. Internationally, the EU has expressed alarm over Chinese technological penetration and its implications for Serbia's EU accession goals.

location information, which is one important aspect of ubiquitous computing. In all its facets like user mobility, terminal and session mobility it requires advances in wireless, ad hoc and selforganizing network technologies and smart sensors and devices. Furthermore, the development of infrastructures becomes more and more important for supporting cognitive environments, as well as discovery and execution frameworks for location-aware services. Context-awareness, a generalization of locationawareness, is another driver of the ubiquitous computing paradigm. Well-designed context modeling and context retrieval approaches are key prerequisites in any context-aware system. From the technical side, sensing, fusing and distributing location and other context information is as important as providing context-awareness to applictions and services in ubiquitous systems. The first International Workshop on Locationand Context-Awareness was held in cooperation with the third International Conference on Pervasive Computing 2005 in Oberpfaffenhofen/Munich, Germany. The workshop was organized by the Institute of Communications and Navigation of the German Aerospace Center (DLR), Oberpfaffenhofen, and the Mobile and Distributed Systems Group of the Ludwig Maximilian University of Munich. A total of 84 papers from 26 countries were submitted, from which 26 full and 7 short papers were selected for publication in the proceedings, printed in the Lecture Notes in Computer Science Series, Volume 3479 by Springer. During the workshop, novel positioning algorithms and location sensing techniques were discussed, comprising not only enhancements of singular systems, like positioning in GSM or WLAN, but also hybrid technologies, such as the integration of global satellite systems with inertial positioning. Furthermore, improvements in sensor technology as well as the integration and fusion of sensors were addressed both on a theoretical and on an implementational level. Personal and confidential data, such as location data of users, have profound implications for personal information privacy. Thus privacy protection, privacyoriented location-aware systems, and how privacy affects the feasibility and usefulness of systems were also addressed in the workshop. By organizing this special issue, we present recent and significant developments in the area of location and context awareness. The articles that this special issue features have been selected from the presentations given at this first Workshop on Location and Context Awareness. Nine Authors were asked to extend their articles based on the discussion and comments made during presentations at the workshop and their latest findings. These submissions have been reviewed once more by, 2–3 reviewers. And finally, seven of these papers have been selected for the publication in this issue. We would like to thank the authors who submitted their articles to this special issue. In addition, we would like to thank the referees for their time and efforts to review these submissions. C. Linnhoff-Popien (&) T. Strang (&) Munich, Germany e-mail: linnhoff@ifi.lmu.de

In recent years, machine learning and deep learning have made remarkable progress and are now widely applied in various fields, including image classification, autonomous driving, natural language processing, and medical diagnosis. However, training these models requires large datasets, which often contain substantial amounts of sensitive personal information, such as medical records and financial details. Without effective privacy protection measures during model training, the risk of sensitive data leakage increases, potentially resulting in severe privacy violations and a loss of trust. As an innovative data representation technique, the Negative Database has proven to be an effective solution in privacy-sensitive domains. Negative databases can be derived from SAT (Boolean Satisfiability Problem) instances, and the hardness of these instances is directly correlated with the level of data protection provided by the Negative Databases. Developing efficient SAT instance generation algorithms to create harder SAT instances can significantly enhance the privacy protection capabilities of the Negative Database. This paper analyzes the hardness conditions of SAT solvers using the Conflict-Driven Clause Learning strategy and proposes a two-stage SAT instance generation algorithm to generate harder SAT instances. These hard instances not only aid in constructing more secure negative databases for enhanced privacy protection but also provide valuable test cases for evaluating and improving SAT solvers.

Estimating the proportion of non-heterosexuals in Taiwan using Christofides’ randomized response model: A comparison of different estimation methods

To safeguard user privacy, critical Internet traffic is often transmitted using encryption. While encryption is crucial for protecting sensitive information, it poses challenges for traffic identification and poses hidden dangers to network security. As a result, the precise classification of encrypted network traffic has become a crucial problem in network security. In light of this, our paper proposes an encrypted traffic identification method based on the C-LSTM model for encrypted traffic recognition by leveraging the power of deep learning. This method can effectively extract spatial and temporal features from encrypted traffic, enabling accurate identification of traffic types. Through rigorous testing and evaluation, our system has achieved an impressive accuracy rate of 96.4% on the widely used ISCXVPN2016 dataset. This achievement demonstrates the effectiveness and reliability of our method in accurately classifying encrypted network traffic. By addressing the challenges posed by encrypted traffic identification, our research contributes to enhancing network security and privacy protection.

BackgroundPulse oximeter apps became of interest to consumers during the COVID-19 pandemic, particularly when traditional over-the-counter pulse oximeter devices were in short supply. Yet, no study to date has examined or scoped the state of privacy policies and notices for the top-rated and most downloaded pulse oximeter apps during COVID-19.ObjectiveThe aim of this study was to examine, through a high-level qualitative assessment, the state and nature of privacy policies for the downloaded and top-rated pulse oximeter apps during the COVID-19 pandemic to (1) compare findings against comparable research involving other mobile health (mHealth) apps and (2) begin discussions on opportunities for future research or investigation.MethodsDuring August-October 2020, privacy policies were reviewed for pulse oximeter apps that had either at least 500 downloads (Google Play Store apps only) or a three out of five-star rating (Apple Store apps only). In addition to determining if the apps had an accessible privacy policy, other key privacy policy–related details that were extracted included, but were not limited to, app developer location (country); whether the app was free or required paid use/subscription; whether an ads disclosure was provided on the app’s site; the scope of personal data collected; proportionality, fundamental rights, and data protection and privacy issues; and privacy safeguards.ResultsSix pulse oximeter apps met the inclusion criteria and only 33% (n=2) of the six apps had an accessible privacy policy that was specific to the pulse oximeter app feature (vs the app developer’s website or at all). Variation was found in both the regulatory nature and data privacy protections offered by pulse oximeter apps, with notable privacy protection limitations and gaps, although each app provided at least some information about the scope of personal data collected upon installing the app.ConclusionsPulse oximeter app developers should invest in offering stronger privacy protections for their app users, and should provide more accessible and transparent privacy policies. This is a necessary first step to ensure that the data privacy of mHealth consumers is not exploited during public health emergency situations such as the COVID-19 pandemic, where over-the-counter personal health monitoring devices could be in short supply and patients and consumers may, as a result, turn to mHealth apps to fill such supply gaps. Future research considerations and recommendations are also suggested for mHealth technology and privacy researchers who are interested in examining privacy implications associated with the use of pulse oximeter apps during and after the COVID-19 pandemic.

In this era of big data, seamless distribution of healthcare information is crucial for improving patient care and advancing medical research, necessitating meticulous attention to preserving health data privacy. However, overly stringent protection measures can impede the efficient utilization of invaluable resources for medical research and personalized healthcare, posing a central challenge in balancing privacy protection with effective data utilization. This study aims to explore various methods used to protect the privacy of patients’ health records, and evaluates their advantages and limitations. Additionally, it conducts an in-depth analysis of a public medical dataset concerning privacy protection, assessing the effectiveness of k-anonymity and l-diversity privacy criteria and examining the influence of quasi-identifier (QID) attributes on privacy preservation. The study showcases techniques to achieve privacy standards, including generalization and suppression. Furthermore, it introduces a novel approach that utilizes the genetic algorithm (GA) and a non-dominated sorting technique to maximize both privacy and utility in health data through multi-objective optimization. After examining the results, this paper offers a guide for data owners on selecting attributes for medical data publication and choosing suitable privacy preservation strategies. Through the exploration of the GA and the non-dominated sorting approach, this paper suggests that the proposed GA can offer promising non-dominated solutions to the issue of health data privacy in the era of data-driven healthcare. A combination of these algorithms can enhance privacy protection and provide healthcare professionals and researchers with essential knowledge, ultimately benefiting patient care and ensuring a more secure database system.