Privacy-related Information Research Articles

A novel privacy protection method of residents' travel trajectories based on federated blockchain and InterPlanetary file systems in smart cities.

The government does have to record and analyze the travel trajectories of urban residents aiming to effectively control the epidemic during COVID-19. However, these privacy-related data are usually stored in centralized cloud databases, which are prone to be vulnerable to cyber attacks leading to personal trajectory information leakage. In this article, we proposed a novel secure sharing and storing method of personal travel trajectory data based on BC and InterPlanetary File System (IPFS). We adopt the Hyperledger Fabric, the representative of Federated BC framework, combined with the IPFS storage to form a novel mode of querying on-chain and storing off-chain aiming to both achieve the effectiveness of data processing and protect personal privacy-related information. This method firstly solves the efficiency problem of traditional public BC and ensures the security of stored data by storing the ciphertext of complete personal travel trajectory data in decentralized IPFS storage. Secondly, considering the huge amount of information of residents' travel trajectories, the method proposed in this article can obtain the complete information under the chain stored in IPFS by querying the index on the chain, which significantly improves the data processing efficiency of residents' travel trajectories and thus promotes the effective control of the new crown pneumonia epidemic. Finally, the feasibility of the proposed solution is verified through performance evaluation and security analysis.

The Privacy Flag Observatory: A Crowdsourcing Tool for Real Time Privacy Threats Evaluation

Instilling good privacy practices to developers and users appears to be a difficult and daunting task. The World Wide Web encompasses a panspermia of different technologies, commercial and open source apis, evolving security standards and protocols that can be deployed towards the implementation of complex, powerful, web applications. At the same time, the proliferation of applications and services on all types of devices has also increased the attack surface for privacy threats. In this paper, we present the Privacy Flag Observatory, a platform which is one of the main tools produced by the Privacy Flag eu funded research project. The goal of this initiative is to raise awareness among European citizens of the potential privacy threats that beset the software and services they trust and use every day, including websites and smartphone applications. The Privacy Flag Observatory is one of the components that contributed to a large extent, to the success of the project’s goals. It is a real-time security and privacy threat monitoring platform whose aim is to collect, archive, analyze and present security and privacy-related information to the broader public as well as experts. Although the platform relies on crowdsourcing information gathering strategies and interacts with several other components installed on users’ devices or remote servers and databases, in this paper, we focus on the observatory platform referring only cursorily to other components such as the mobile phone add-on.

Secure Electronic Healthcare Record based on Distributed Global Database and Schnorr Signcryption

Preserving privacy and security plays a key role in allowing each component in the healthcare system to access control and gain privileges for services and resources. Over recent years, there have been several role-based access control and authentication schemes, but we noticed some drawbacks in target schemes such as failing to resist well-known attacks, leaking privacy-related information, and operational cost. To defeat the weakness, this paper proposes a secure electronic healthcare record scheme based on Schnorr Signcryption, crypto hash function, and Distributed Global Database (DGDB) for the healthcare system. Based on security theories and the Canetti-Krawczyk model (CK), we notice that the proposed scheme has suitable matrices such as scalability, privacy preservation, and mutual authentication. Furthermore, findings from comparisons with comparable schemes reveal that the suggested approach provides greater privacy and security characteristics than the other schemes and has enough efficiency in computational and communicational aspects.

A privacy scoring framework: Automation of privacy compliance and risk evaluation with standard indicators

Personal data have become the key to data-driven services and applications whereas privacy requirements are now strongly imposed by regulations. Meanwhile, people find it difficult to understand whether the services and applications handle personal data to comply with their agreements and regulations. Therefore, the need for privacy indicators, which summarize privacy contents as forms of privacy scoring, labels, etc., has increased to empower the users’ rights by providing understandable information about privacy. For firm privacy indicators, proper criteria and methods for evaluating the level of privacy risks and compliance are required. Accordingly, this paper proposes a privacy scoring framework for services in the context of handling personal data, inspired by six standardized indicators. This paper introduces detailed information on standardized indicators and proposes privacy indicators to quantify privacy scores. Also, this paper proposes methods for evaluating privacy policy based on a set of machine learning-based hierarchical binary classifiers and processes for quantifying the level of privacy risks and compliance from privacy-related information. Through analyzing privacy policies and data access lists of more than 10,000 mobile applications on Google Play Store and investigating case studies on privacy scoring of some mobile applications, this paper shows the feasibility of the proposed framework.

A Privacy Preserving Generative Adversarial Network for Image Data

The extensive usage of online applications and social media has raised serious concerns from the public regarding the exposure of their personal information. So, there is a strong need for data anonymization to prevent privacy breaches and leakages. The era of attacks on databases and servers is an old trend. Now, most attacks are based on earning access to users’ private data. There are techniques like k-anonymity and l-diversity to protect Personally Identifiable Information (PII) from adversaries. However, these techniques still cannot provide security from homogeneity attacks, and their application is limited to structural data only. Till now, the frameworks are only available to anonymize the human face data in image format. In this paper, we proposed a new architecture for protecting privacy-related information in images of Indian vehicle number plates. We propose an architecture for anonymizing the vehicle number plates using Wasserstein’s Generative Adversarial Network (WGAN) by retaining the original data distribution even after anonymization. Our framework guarantees that it does not store any information while processing. Our main goal is to protect personal information from the image data. After anonymization, there is no similarity between the original and generated image. Our dataset includes a wide variety of license plates from all regions of India. Our work ensures that no human or a character recognition algorithm can recognize the characters from our anonymized images.

Privacy leakage of search-based multi-agent planning algorithms

Privacy preservation has become one of the crucial research topics in multi-agent planning. A number of techniques to preserve private information throughout the planning process have emerged. One major difficulty of such research is the comparison of properties related to privacy among such techniques. A metric allowing for comparison of such privacy preservation was introduced only recently, having a number of drawbacks such as prohibitive computational complexity. In this work we strengthen the theoretical foundations and simplify the metric in order to be practically usable. Moreover, we test the usability of the metric in an analysis of various techniques in multi-agent heuristic computation and search, determining which are the most beneficial in terms of privacy preservation. We also evaluate the techniques in terms of the classical IPC score to assess their impact on the overall planning performance. The results are somewhat surprising and show that extracting any privacy-related information even from the simplest variant of heuristic search is a very complicated task. Existing techniques such as distributed heuristic and sending only relevant states is shown to reduce the privacy leakage even more.

Detecting privacy requirements from User Stories with NLP transfer learning models

To provide privacy-aware software systems, it is crucial to consider privacy from the very beginning of the development. However, developers do not have the expertise and the knowledge required to embed the legal and social requirements for data protection into software systems. Objective: We present an approach to decrease privacy risks during agile software development by automatically detecting privacy-related information in the context of user story requirements, a prominent notation in agile Requirement Engineering (RE). Methods: The proposed approach combines Natural Language Processing (NLP) and linguistic resources with deep learning algorithms to identify privacy aspects into User Stories. NLP technologies are used to extract information regarding the semantic and syntactic structure of the text. This information is then processed by a pre-trained convolutional neural network, which paved the way for the implementation of a Transfer Learning technique. We evaluate the proposed approach by performing an empirical study with a dataset of 1680 user stories. Results: The experimental results show that deep learning algorithms allow to obtain better predictions than those achieved with conventional (shallow) machine learning methods. Moreover, the application of Transfer Learning allows to considerably improve the accuracy of the predictions, ca. 10%. Conclusions: Our study contributes to encourage software engineering researchers in considering the opportunities to automate privacy detection in the early phase of design, by also exploiting transfer learning models.

Privacy-Preserving Authentication Protocol for Wireless Body Area Networks in Healthcare Applications.

Mobile healthcare service has become increasingly popular thanks to the significant advances in the wireless body area networks (WBANs). It helps medical professionals to collect patient’s healthcare data remotely and provides remote medical diagnosis. Since the health data are privacy-related, they should provide services with privacy-preserving, which should consider security and privacy at the same time. Recently, some lightweight patient healthcare authentication protocols were proposed for WBANs. However, we observed that they are vulnerable to tracing attacks because the patient uses the same identifier in each session, which could leak privacy-related information on the patient. To defeat the weakness, this paper proposes a privacy-preserving authentication protocol for WBANs in healthcare service. The proposed protocol is only based on one-way hash function and with exclusive-or operation, which are lightweight operations than asymmetric cryptosystem operations. We performed two rigorous formal security proofs based on BAN logic and ProVerif tool. Furthermore, comparison results with the relevant protocols show that the proposed protocol achieves more privacy and security features than the other protocols and has suitable efficiency in computational and communicational concerns.

Event Encryption for Neuromorphic Vision Sensors: Framework, Algorithm, and Evaluation.

Nowadays, our lives have benefited from various vision-based applications, such as video surveillance, human identification and aided driving. Unauthorized access to the vision-related data greatly threatens users’ privacy, and many encryption schemes have been proposed to secure images and videos in those conventional scenarios. Neuromorphic vision sensor (NVS) is a brand new kind of bio-inspired sensor that can generate a stream of impulse-like events rather than synchronized image frames, which reduces the sensor’s latency and broadens the applications in surveillance and identification. However, the privacy issue related to NVS remains a significant challenge. For example, some image reconstruction and human identification approaches may expose privacy-related information from NVS events. This work is the first to investigate the privacy of NVS. We firstly analyze the possible security attacks to NVS, including grayscale image reconstruction and privacy-related classification. We then propose a dedicated encryption framework for NVS, which incorporates a 2D chaotic mapping to scramble the positions of events and flip their polarities. In addition, an updating score has been designed for controlling the frequency of execution, which supports efficient encryption on different platforms. Finally, extensive experiments have demonstrated that the proposed encryption framework can effectively protect NVS events against grayscale image reconstruction and human identification, and meanwhile, achieve high efficiency on various platforms including resource-constrained devices.

Analysis of Diabetes Apps to Assess Privacy-Related Permissions: Systematic Search of Apps.

BackgroundMobile health has become a major vehicle of support for people living with diabetes. Accordingly, the availability of mobile apps for diabetes has been steadily increasing. Most of the previous reviews of diabetes apps have focused on the apps’ features and their alignment with clinical guidelines. However, there is a lack of knowledge on the actual compliance of diabetes apps with privacy and data security guidelines.ObjectiveThe aim of this study was to assess the levels of privacy of mobile apps for diabetes to contribute to the raising of awareness of privacy issues for app users, developers, and governmental data protection regulators.MethodsWe developed a semiautomatic app search module capable of retrieving Android apps’ privacy-related information, particularly the dangerous permissions required by apps, with the aim of analyzing privacy aspects related to diabetes apps. Following the research selection criteria, the original 882 apps were narrowed down to 497 apps that were included in the analysis.ResultsApproximately 60% of the analyzed diabetes apps requested potentially dangerous permissions, which pose a significant risk to users’ data privacy. In addition, 28.4% (141/497) of the apps did not provide a website for their privacy policy. Moreover, it was found that 40.0% (199/497) of the apps contained advertising, and some apps that claimed not to contain advertisements actually did. Ninety-five percent of the apps were free, and those belonging to the “medical” and “health and fitness” categories were the most popular. However, app users do not always realize that the free apps’ business model is largely based on advertising and, consequently, on sharing or selling their private data, either directly or indirectly, to unknown third parties.ConclusionsThe aforementioned findings confirm the necessity of educating patients and health care providers and raising their awareness regarding the privacy aspects of diabetes apps. Therefore, this research recommends properly and comprehensively training users, ensuring that governments and regulatory bodies enforce strict data protection laws, devising much tougher security policies and protocols in Android and in the Google Play Store, and implicating and supervising all stakeholders in the apps’ development process.

CLEANIR: Controllable Attribute-Preserving Natural Identity Remover

We live in an era of privacy concerns. As smart devices such as smartphones, service robots and surveillance cameras spread, preservation of our privacy becomes one of the major concerns in our daily life. Traditionally, the problem was resolved by simple approaches such as image masking or blurring. While these provide effective ways to remove identities from images, there are certain limitations when it comes to a matter of recognition from the processed images. For example, one may want to get ambient information from scenes even when privacy-related information such as facial appearance is removed or changed. To address the issue, our goal in this paper is not only to modify identity from faces but also keeps facial attributes such as color, pose and facial expression for further applications. We propose a novel face de-identification method based on a deep generative model in which we design the output vector from an encoder to be disentangled into two parts: identity-related part and the rest representing facial attributes. We show that by solely modifying the identity-related part from the latent vector, our method effectively modifies the facial identity to a completely new one while the other attributes that are loosely related to personal identity are preserved. To validate the proposed method, we provide results from experiments that measure two different aspects: effectiveness of personal identity modification and facial attribute preservation.

Sight unseen: The role of online security indicators in visual attention to online privacy information

Although data privacy is a compelling concern, prior inquiries have found that consumers do not adequately attend to data privacy policies. This research develops a framework delineating the effects of top-down/bottom-up processes and individual consumer differences in the need for cognition (NFC) and perceived control on visual attention to data privacy policies, including influences on perceptions, attitudes, and behavioral intentions. Using eye tracking in an online shopping context, this research finds perceived control interacted with top-down motivation, manipulated through perceived risk in sharing personal information, to affect visual attention toward privacy-related information. Moreover, a privacy policy icon garners more attention than privacy policy text and non-privacy contents on the website. Further, attention to non-privacy contents significantly enhances loan application likelihood and attitude toward the website. These findings demonstrate attention toward privacy information as a function of the dynamic interaction between top-down motivational influences and individual differences in perceived control.

Privacy Engineering for Domestic IoT: Enabling Due Diligence.

The EU’s General Data Protection Regulation (GDPR) has recently come into effect and insofar as Internet of Things (IoT) applications touch EU citizens or their data, developers are obliged to exercise due diligence and ensure they undertake Data Protection by Design and Default (DPbD). GDPR mandates the use of Data Protection Impact Assessments (DPIAs) as a key heuristic enabling DPbD. However, research has shown that developers generally lack the competence needed to deal effectively with legal aspects of privacy management and that the difficulties of complying with regulation are likely to grow considerably. Privacy engineering seeks to shift the focus from interpreting texts and guidelines or consulting legal experts to embedding data protection within the development process itself. There are, however, few examples in practice. We present a privacy-oriented, flow-based integrated development environment (IDE) for building domestic IoT applications. The IDE enables due diligence in (a) helping developers reason about personal data during the actual in vivo construction of IoT applications; (b) advising developers as to whether or not the design choices they are making occasion the need for a DPIA; and (c) attaching and making available to others (including data processors, data controllers, data protection officers, users and supervisory authorities) specific privacy-related information that has arisen during an application’s development.

Individuals’ Stress Assessment Using Human-Smartphone Interaction Analysis

The increasing presence of stress in people’ lives has motivated much research efforts focusing on continuous stress assessment methods of individuals, leveraging smartphones and wearable devices. These methods have several drawbacks, i.e., they use invasive external devices, thus increasing entry costs and reducing user acceptance, or they use some of privacy-related information. This paper presents an approach for stress assessment that leverages data extracted from smartphone sensors, and that is not invasive concerning privacy. Two different approaches are presented. One, based on smartphone gestures analysis, e.g., ‘tap’, ‘scroll’, ‘swipe’ and ‘text writing’, and evaluated in laboratory settings with 13 participants (F-measure 79-85 percent within-subject model, 70-80 percent global model); the second one based on smartphone usage analysis and tested in-the-wild with 25 participants (F-measure 77-88 percent within-subject model, 63-83 percent global model). Results show how these two methods enable an accurate stress assessment without being too intrusive, thus increasing ecological validity of the data and user acceptance.

Research Commentary—Informing Privacy Research Through Information Systems, Psychology, and Behavioral Economics: Thinking Outside the “APCO” Box

Recently, several researchers provided overarching macromodels to explain individuals’ privacy-related decision making. These macromodels—and almost all of the published privacy-related information systems (IS) studies to date—rely on a covert assumption: responses to external stimuli result in deliberate analyses, which lead to fully informed privacy-related attitudes and behaviors. The most expansive of these macromodels, labeled “Antecedents–Privacy Concerns–Outcomes” (APCO), reflects this assumption. However, an emerging stream of IS research demonstrates the importance of considering principles from behavioral economics (such as biases and bounded rationality) and psychology (such as the elaboration likelihood model) that also affect privacy decisions. We propose an enhanced APCO model and a set of related propositions that consider both deliberative (high-effort) cognitive responses (the only responses considered in the original APCO model) and low-effort cognitive responses inspired by frameworks and theories in behavioral economics and psychology. These propositions offer explanations of many behaviors that complement those offered by extant IS privacy macromodels and the information privacy literature stream. We discuss the implications for research that follow from this expansion of the existing macromodels.

Privacy-Preserving H.264 Video Encryption Scheme

As a growing number of individuals are exposed to surveillance cameras, the need to prevent captured videos from being used inappropriately has increased. Privacy-related information can be protected through video encryption during transmission or storage, and several algorithms have been proposed for such purposes. However, the simple way of evaluating the security by counting the number of brute-force trials is not proper for measuring the security of video encryption algorithms, considering that attackers can devise specially crafted attacks for specific purposes by exploiting the characteristics of the target video codec. In this paper, we introduce a new attack for recovering contour information from encrypted H.264 video. The attack can thus be used to extract face outlines for the purpose of personal identification. We analyze the security of previous video encryption schemes against the proposed attack and show that the security of these schemes is lower than expected in terms of privacy protection. To enhance security, an advanced block shuffling method is proposed, an analysis of which shows that it is more secure than the previous method and can be an improvement against the proposed attack.

Collaborative privacy management

The landscape of the World Wide Web with all its versatile services heavily relies on the disclosure of private user information. Unfortunately, the growing amount of personal data collected by service providers poses a significant privacy threat for Internet users. Targeting growing privacy concerns of users, privacy-enhancing technologies emerged. One goal of these technologies is the provision of tools that facilitate a more informative decision about personal data disclosures. A famous PET representative is the PRIME project that aims for a holistic privacy-enhancing identity management system. However, approaches like the PRIME privacy architecture require service providers to change their server infrastructure and add specific privacy-enhancing components. In the near future, service providers are not expected to alter internal processes. Addressing the dependency on service providers, this paper introduces a user-centric privacy architecture that enables the provider-independent protection of personal data. A central component of the proposed privacy infrastructure is an online privacy community, which facilitates the open exchange of privacy-related information about service providers. We characterize the benefits and the potentials of our proposed solution and evaluate a prototypical implementation.

Privacy, Deontic Epistemic Action Logic and Software Agents

In this paper we present an executable approach to model interactions between agents that involve sensitive, privacy-related information. The approach is formal and based on deontic, epistemic and action logic. It is conceptually related to the Belief-Desire-Intention model of Bratman. Our approach uses the concept of sphere as developed by Waltzer to capture the notion that information is provided mostly with restrictions regarding its application. We use software agent technology to create an executable approach. Our agents hold beliefs about the world, have goals and commitment to the goals. They have the capacity to reason about different courses of action, and communicate with one another. The main new ingredient of our approach is the idea to model information itself as an intentional agent whose main goal it is to preserve the integrity of the information and regulate its dissemination. We demonstrate our approach by applying it to an important process in the insurance industry: applying for a life insurance. In this paper we will: (1) describe the challenge organizational complexity poses in moral reasoning about informational relationships; (2) propose an executable approach, using software agents with reasoning capacities grounded in modal logic, in which moral constraints on informational relatio nships can be modeled and investigated; (3) describe the details of our approach, in which information itself is modeled as an intentional agent in its own right; (4) test and validate it by applying it to a concrete `hard case' from the insurance industry; and (5) conclude that our approach upholds and offers potential for both research and practical application.