Privacy Tools Research Articles

Security and Privacy in Healthcare Applications Using Federated Learning - Review

This review explored data security and privacy in Internet of Health Things (IoHT) networks, focusing on local training for AI models and Federated Learning (FL) to ensure privacy and integrity. This highlights the importance of leveraging technological advancements for disease prediction and data exchange. Functional programming can be used in various industries and applications under real-world scenarios. In the medical industry, safeguarding the confidentiality of patient records and medical status is essential. This is where collaborative or federated learning becomes relevant. On the other hand, creating an intelligent system that helps medical personnel without exposing data can result in a Federated Learning concept. An example is an AI-based intelligent system for diagnosing brain tumors, which can effectively operate within a teamwork setting. Advancements in smart devices and applications designed for the Internet of Healthcare Things have created an ideal setting for implementing Machine Learning techniques. Nevertheless, conventional ML solutions work by collecting and processing data in a centralized manner. Federated Learning (FL) offers a potential solution for training machine-learning models on numerous separate devices without the need to share private data. As a result, FL provides a secure framework for managing extremely sensitive data in the context of the IoHT. This survey provides a detailed overview of new data security and privacy tools for Federated Learning in Internet of Health Things networks. Initially, we introduced the fundamental concepts of Federated Learning (FL) as it is applied to the IoHT.

Confidential Computing Proofs

Proofs are powerful tools for integrity and privacy, enabling the verifier to delegate a computation and still verify its correct execution, and enabling the prover to keep the details of the computation private. Both CCP and ZKP can achieve soundness and zero-knowledge but with important differences. CCP relies on hardware trust assumptions, which yield high performance and additional confidentiality protection for the prover but may be unacceptable for some applications. CCP is also often easier to use, notably with existing code, whereas ZKP comes with a large prover overhead that may be unpractical for some applications.

A Differentially Private Framework for the Dynamic Heterogeneous Redundant Architecture System in Cyberspace

With the development of information technology, tremendous vulnerabilities and backdoors have evolved, causing inevitable and severe security problems in cyberspace. To fix them, the endogenous safety and security (ESS) theory and one of its practices, the Dynamic Heterogeneous Redundant (DHR) architecture, are proposed. In the DHR architecture, as an instance of the multi-heterogeneous system, a decision module is designed to obtain intermediate results from heterogeneous equivalent functional executors. However, privacy-preserving is not paid attention to in the architecture, which may cause privacy breaches without compromising the ESS theory. In this paper, based on differential privacy (DP), a theoretically rigorous privacy tool, we propose a privacy-preserving DHR framework called DP-DHR. Gaussian random noise is injected into each (online) executor output in DP-DHR to guarantee DP, but it also makes the decision module unable to choose the final result because each executor output is potentially correct even if it is compromised by adversaries. To weaken this disadvantage, we propose the advanced decision strategy and the hypersphere clustering algorithm to classify the perturbed intermediate results into two categories, candidates and outliers, where the former is closer to the correct value than the latter. Finally, the DP-DHR is proven to guarantee DP, and the experimental results also show that the utility is not sacrificed for the enhancement of privacy by much (a ratio of 4–7% on average), even in the condition of some executors (less than one-half) being controlled by adversaries.

Analysis Of The 2nd Principle Of Pancasila On The Instagram Privacy Disclaimer

The article discusses the importance of user privacy on Instagram, a popular social media platform. In the digital era, millions of users share aspects of their personal lives, creating risks of data misuse and security threats. The right to privacy is recognized as a human right, including in the Indonesian Privacy Rights Law. Privacy Controls: Instagram gives users full control, allowing personal account settings. Data Policy: Instagram has a policy that explains the collection, use, and sharing of data, reflecting civilized principles. Data Security: Instagram claims to implement security measures and collaborate with third parties. Privacy Tools and Features: Instagram is constantly developing new privacy tools and features. In the context of Instagram user privacy, the principle of the 2nd Principle, "Just and Civilized Humanity," reflects fair treatment and respect for human rights. The rights to privacy, freedom of expression, data security, and control over personal information are the emphasis in implementing the 2nd Principle. Assessing the use of Instagram's privacy policy by the 2nd Principle and encouraging users to be wise in sharing information. A mixed method approach with descriptive qualitative methods and literature studies is used to understand the impact of Instagram user privacy. Privacy is a human right, and Instagram is expected to implement the principles of the 2nd Principle with policies that support the control, security, and fair treatment of users' data.

Platform rules as privacy tools: The influence of screenshot accountability and trust on privacy management

Society has normalized the use of the screenshot feature to surreptitiously capture and share private digital messages. While screenshots have utilitarian purposes, we provide evidence that this feature enables violations of interpersonal privacy expectations. In addition, we extend communication privacy management (CPM) theory beyond its interpersonal limitations to include platforms as privacy rulemakers through explicit cues and embedded trust. We conducted a 2(Accountability cue: present or absent) × 3(Platform trust: high, low, control) between-subjects experiment to understand their conditional impact on control over and disclosure of personal and co-owned information within a proposed digital messaging platform ( N = 307). Our experimental results showcase the power of a screenshot accountability cue and platform trust on privacy perceptions and management within messaging platforms. Implications for design against the screenshot feature are discussed.

Evaluation of Open-Source Tools for Differential Privacy

Differential privacy (DP) defines privacy protection by promising quantified indistinguishability between individuals who consent to share their privacy-sensitive information and those who do not. DP aims to deliver this promise by including well-crafted elements of random noise in the published data, and thus there is an inherent tradeoff between the degree of privacy protection and the ability to utilize the protected data. Currently, several open-source tools have been proposed for DP provision. To the best of our knowledge, there is no comprehensive study for comparing these open-source tools with respect to their ability to balance DP’s inherent tradeoff as well as the use of system resources. This work proposes an open-source evaluation framework for privacy protection solutions and offers evaluation for OpenDP Smartnoise, Google DP, PyTorch Opacus, Tensorflow Privacy, and Diffprivlib. In addition to studying their ability to balance the above tradeoff, we consider discrete and continuous attributes by quantifying their performance under different data sizes. Our results reveal several patterns that developers should have in mind when selecting tools under different application needs and criteria. This evaluation survey can be the basis for an improved selection of open-source DP tools and quicker adaptation of DP.

Lessons Learned: Surveying the Practicality of Differential Privacy in the Industry

Since its introduction in 2006, differential privacy has emerged as a predominant statistical tool for quantifying data privacy in academic works. Yet despite the plethora of research and open-source utilities that have accompanied its rise, with limited exceptions, differential privacy has failed to achieve widespread adoption in the enterprise domain. Our study aims to shed light on the fundamental causes underlying this academic-industrial utilization gap through detailed interviews of 24 privacy practitioners across 9 major companies. We analyze the results of our survey to provide key findings and suggestions for companies striving to improve privacy protection in their data workflows and highlight the necessary and missing requirements of existing differential privacy tools, with the goal of guiding researchers working towards the broader adoption of differential privacy. Our findings indicate that analysts suffer from lengthy bureaucratic processes for requesting access to sensitive data, yet once granted, only scarcely-enforced privacy policies stand between rogue practitioners and misuse of private information. We thus argue that differential privacy can significantly improve the processes of requesting and conducting data exploration across silos, and conclude that with a few of the improvements suggested herein, the practical use of differential privacy across the enterprise is within striking distance.

Effective Techniques for Protecting the Privacy of Web Users

With the rapid growth of web networks, the security and privacy of online users are becoming more compromised. Especially, the use of third-party services to track users’ activities and improve website performance. Therefore, it is unavoidable that using personal information to create unique profiles may violate individuals’ privacy. Recently, several tools have been developed such as anonymity, anti-tracking, and browser plugins to ensure the protection of users from third-party tracking methods by blocking JavaScript programs and other website components. However, the current state lacks an efficient approach that provides a comprehensive solution. In this paper, we conducted a systematic analysis of the most common privacy protection tools based on their accuracy and performance by evaluating their effectiveness in correctly classifying tracking and functional JavaScript programs, then evaluating the estimated time the browser takes to render the pages for each tool. To achieve this, we automatically browsed the most 50 websites determined in 2022 and categorized them according to different fields to get the in-page (as part of HTML script tags), and all external JavaScript programs. Then we collected data and datasets of 1578 JavaScript elements and obtained six diverse Firefox profiles when the tools were enabled. The results found that Ghostery has the highest percentage of allowing most functioning scripts with the lowest average error rate (AER). While at the same time NoScript achieved the highest percentage of blocking most tracking scripts since it is the highest blocker of third-party services. After that, we examined the speed of the browser finding that, Ghostery improved the load time by 36.2% faster than the baseline, while Privacy Badger only reduced the load time by 7.1%. We believe that our findings can help users decide on a privacy tool that meets their needs. Moreover, researchers and developers can use our findings to improve the privacy of internet users by designing more effective privacy protection techniques.

Enhancing user awareness on inferences obtained from fitness trackers data.

In the IoT era, sensitive and non-sensitive data are recorded and transmitted to multiple service providers and IoT platforms, aiming to improve the quality of our lives through the provision of high-quality services. However, in some cases these data may become available to interested third parties, who can analyse them with the intention to derive further knowledge and generate new insights about the users, that they can ultimately use for their own benefit. This predicament raises a crucial issue regarding the privacy of the users and their awareness on how their personal data are shared and potentially used. The immense increase in fitness trackers use has further increased the amount of user data generated, processed and possibly shared or sold to third parties, enabling the extraction of further insights about the users. In this work, we investigate if the analysis and exploitation of the data collected by fitness trackers can lead to the extraction of inferences about the owners routines, health status or other sensitive information. Based on the results, we utilise the PrivacyEnhAction privacy tool, a web application we implemented in a previous work through which the users can analyse data collected from their IoT devices, to educate the users about the possible risks and to enable them to set their user privacy preferences on their fitness trackers accordingly, contributing to the personalisation of the provided services, in respect of their personal data.

The Role of Data Leakage Prevention System in CBDC

centralized database-based Central Bank Digital Currency (CBDC) system's vulnerability to cyberattacks and data leakage is a major concern. Any data leak can lead to large financial losses, irreversible reputational harm, and a decline in user confidence. To protect user information, the Reserve Bank of India has underlined the significance of a strong Data Leakage Prevention (DLP) system. While current incidents have demonstrated that the measures were insufficient to meet the standards, DLP may not be enough to defend CBDCs on its own. Incorporating Zero-Knowledge Proofs (ZKPs) and differential privacy tools into DLP solutions can improve their robustness and effectiveness. There is no one-size-fits-all solution for preventing data leakage, different solutions may be more effective in different scenarios. It's always a good idea to assess an organization's or system's specific needs and requirements before deciding on the best solution. It is also important to remember that there is no such thing as absolute security, and the possibility of zero-day attacks is always there. It is essential to continuously monitor and enhance security measures to stay ahead of new threats. To preserve their Central Bank Digital Currency systems and data, financial institutions and central banks must continue to be proactive and vigilant.

“All apps do this”: Comparing Privacy Concerns Towards Privacy Tools and Non-Privacy Tools for Social Media Content

Users report that they have regretted accidentally sharing personal information on social media. There have been proposals to help protect the privacy of these users, by providing tools which analyze text or images and detect personal information or privacy disclosure with the objective to alert the user of a privacy risk and transform the content. However, these proposals rely on having access to users’ data and users have reported that they have privacy concerns about the tools themselves. In this study, we investigate whether these privacy concerns are unique to privacy tools or whether they are comparable to privacy concerns about non-privacy tools that also process personal information. We conduct a user experiment to compare the level of privacy concern towards privacy tools and nonprivacy tools for text and image content, qualitatively analyze the reason for those privacy concerns, and evaluate which assurances are perceived to reduce that concern. The results show privacy tools are at a disadvantage: participants have a higher level of privacy concern about being surveilled by the privacy tools, and the same level concern about intrusion and secondary use of their personal information compared to non-privacy tools. In addition, the reasons for these concerns and assurances that are perceived to reduce privacy concern are also similar. We discuss what these results mean for the development of privacy tools that process user content.

Survey of privacy-preserving mechanisms for heterogeneous data types

Due to the pervasiveness of always connected devices, large amounts of heterogeneous data are continuously being collected. Beyond the benefits that accrue for the users, there are private and sensitive information that is exposed. Therefore, Privacy-Preserving Mechanisms (PPMs) are crucial to protect users’ privacy. In this paper, we perform a thorough study of the state of the art on the following topics: heterogeneous data types, PPMs, and tools for privacy protection. Building from the achieved knowledge, we propose a privacy taxonomy that establishes a relation between different types of data and suitable PPMs for the characteristics of those data types. Moreover, we perform a systematic analysis of solutions for privacy protection, by presenting and comparing privacy tools. From the performed analysis, we identify open challenges and future directions, namely, in the development of novel PPMs.

Applying Artificial Intelligence Privacy Technology in the Healthcare Domain.

Regulations set out strict restrictions on processing personal data. ML models must also adhere to these restrictions, as it may be possible to infer personal information from trained models. In this paper, we demonstrate the use of two novel AI Privacy tools in a real-world healthcare application.

Design Evolution of a Tool for Privacy and Security Protection for Activists Online: Cyberactivist

This work forms a part of a series of “on the ground” studies dealing with (post-)conflict situations, focusing on the iterative, participatory design of a tool, Cyberactivist, for protection for activists and the empirical research that led to it. Work on the development of privacy and security tools has not always recognized the fragile nature of the political processes in emerging democracies, frequent naivety about threat, nor the “occasioned” responses of activists because activism can be a “one time” endeavor, prompted by specific events. Researching political activism in Republika Srpska, we identified issues relating to the use of ICT and social media, leading to the redesign of our prototype which now raises awareness of privacy and security and supports activists by challenging ignorance, lowering exposure, and enabling remediation. We addressed “usable security” challenges to ensure simplicity of the tool and engaged with HCI researchers focused on international activism to assess the global applicability of the technical design.

‘A right to be forgotten’: retrospective privacy concerns in social networking services

ABSTRACT To gain a heuristic understanding of the temporal dimension of privacy, we propose the concept of retrospective privacy concerns and explore its antecedents, through the lens of contextual integrity framework. We collected 276 respondents and then conducted partial least squares to test our hypotheses. The results show life change experiences and information relevance impact retrospective impression management concerns, which, in turn, exert an effect on retrospective privacy concerns. Information dissemination regarding sharing information to only selected friends perceived higher level of control than sharing information to all friends, while perceived control could enhance retrospective privacy concerns. Also, users who feel under surveillance would be concerned about their retrospective privacy. Moreover, we verify the effect of time on retrospective privacy concerns and information relevance. This work provides new insight into the temporal dimension of privacy and offers practical implications for designing temporal privacy tools to mitigate users’ concerns.

Increasing Adoption of Tor Browser Using Informational and Planning Nudges

Abstract Browsing privacy tools can help people protect their digital privacy. However, tools which provide the strongest protections—such as Tor Browser—have struggled to achieve widespread adoption. This may be due to usability challenges, misconceptions, behavioral biases, or mere lack of awareness. In this study, we test the effectiveness of nudging interventions that encourage the adoption of Tor Browser. First, we test an informational nudge based on protection motivation theory (PMT), designed to raise awareness of Tor Browser and help participants form accurate perceptions of it. Next, we add an action planning implementation intention, designed to help participants identify opportunities for using Tor Browser. Finally, we add a coping planning implementation intention, designed to help participants overcome challenges to using Tor Browser, such as extreme website slowness. We test these nudges in a longitudinal field experiment with 537 participants. We find that our PMT-based intervention increased use of Tor Browser in both the short- and long-term. Our coping planning nudge also increased use of Tor Browser, but only in the week following our intervention. We did not find statistically significant evidence of our action planning nudge increasing use of Tor Browser. Our study contributes to a greater understanding of factors influencing the adoption of Tor Browser, and how nudges might be used to encourage the adoption of Tor Browser and similar privacy enhancing technologies.

A Review of the Privacy-Enhancing Technologies Software Market

Cybersecurity is an established business sector with numerous global public companies employing hundreds of thousands of professionals. Although the range of services offered continues to change and expand, experienced security executives understand the types of technologies they will need to develop and retain. In contrast, the privacy technology sector is still maturing. While many privacy tools and services are well developed and numerous businesses have gained traction by solving privacy problems, companies’ needs for privacy technology to help manage and utilize data continue to evolve rapidly. Based on a report by the Future of Privacy Forum and the Privacy Tech Alliance, this article reviews developments in the privacy technology sector and charts the services provided as companies mature. The report draws on dozens of hours of interviews with buyers and sellers as well as conversations with leading privacy tech experts and a survey sent to privacy technology companies.

Collective Privacy Management Practices: A Study of Privacy Strategies and Risks in a Private Facebook Group

This paper qualitatively examines how members of a large private Facebook group view the risks of information disclosure to their privacy and the strategies they employ to navigate and manage those risks. The paper adds to an emerging interest in how privacy is managed collectively and within dynamic large groups, thus moving beyond established knowledge of privacy management on individual and small-scale levels. The work builds on semi-structured interviews with 20 members of a private Facebook group and draws on Communication Privacy Management theory. The study shows how privacy management practices are enacted at individual, intragroup, and group levels. Findings show that participants associate very high risks with sharing private information in the group, partly because it consists of a mix of known others and strangers, who are potentially geographically co-located. They adopt several strategies for managing and protecting their privacy at all three levels. The risks associated with context, time, and spatial collapse of the imagined audience are identified as important to how participants experience information disclosure in the group. The paper concludes by identifying some practical implications that serve as a call for developers to design privacy tools that support dynamic groups' privacy challenges and needs.

Home-Based Telemental Health: A Proposed Privacy and Safety Protocol and Tool.

Objectives: To describe the development of a protocol and practical tool for the safe delivery of telemental health (TMH) services to the home. The COVID-19 pandemic forced providers to rapidly transition their outpatient practices to home-based TMH (HB-TMH) without existing protocols or tools to guide them. This experience underscored the need for a standardized privacy and safety tool as HB-TMH is expected to continue as a resource during future crises as well as to become a component of the routine mental health care landscape. Methods: The authors represent a subset of the Child and Adolescent Psychiatry Telemental Health Consortium. They met weekly through videoconferencing to review published safety standards of care, existing TMH guidelines for clinic-based and home-based services, and their own institutional protocols. They agreed on three domains foundational to the delivery of HB-TMH: environmental safety, clinical safety, and disposition planning. Through multiple iterations, they agreed upon a final Privacy and Safety Protocol for HB-TMH. The protocol was then operationalized into the Privacy and Safety Assessment Tool (PSA Tool) based on two keystone medical safety constructs: the World Health Organization (WHO) Surgical Safety Checklist/Time-Out and the Checklist Manifesto.Results: The PSA Tool comprised four modules: (1) Screening for Safety for HB-TMH; (2) Assessment for Safety During the HB-TMH Initial Visit; (3) End of the Initial Visit and Disposition Planning; and (4) the TMH Time-Out and Reassessment during subsequent visits. A sample workflow guides implementation. Conclusions: The Privacy and Safety Protocol and PSA Tool aim to prepare providers for the private and safe delivery of HB-TMH. Its modular format can be adapted to each site's resources. Going forward, the PSA Tool should help to facilitate the integration of HB-TMH into the routine mental health care landscape.

A new model for forensic data extraction from encrypted mobile devices

In modern criminal investigations, mobile devices are seized at every type of crime scene, and the data on those devices often becomes critical evidence in the case. Various mobile forensic techniques have been established and evaluated through research in order to extract possible evidence data from devices over the decades. However, as mobile devices become essential tools for daily life, security and privacy concerns grow, and modern smartphone vendors have implemented multiple types of security protection measures - such as encryption - to guard against unauthorized access to the data on their products. This trend makes forensic acquisition harder than before, and data extraction from those devices for criminal investigation is becoming a more challenging task. Today, mobile forensic research focuses on identifying more invasive techniques, such as bypassing security features, and breaking into target smartphones by exploiting their vulnerabilities. In this paper, we explain the increased encryption and security protection measures in modern mobile devices and their impact on traditional forensic data extraction techniques for law enforcement purposes. We demonstrate that in order to overcome encryption challenges, new mobile forensic methods rely on bypassing the security features and exploiting system vulnerabilities. A new model for forensic acquisition is proposed. The model is supported by a legal framework focused on the usability of digital evidence obtained through vulnerability exploitation.