Privacy Options Research Articles

CARE, INC.: HOW BIG TECH RESPONDED TO THE END OF ROE

After the leak of the Dobbs decision that ultimately overturned Roe v. Wade, technology companies made a series of public statements in support of user privacy: Apple released an advertisement showcasing privacy features; Google promised to delete location data of abortion clinic visitors; Meta announced testing of default end-to-end-encryption. Corporations like Meta once worked to convince users that their platforms were morally neutral. Now, they publicly “crack down” on manipulation and speak out for racial justice, despite privately subjecting activists to state surveillance. To bolster their authority and popularity, platforms engage in “commodity activism,” in which corporations take positions on social issues. Ultimately, this enhances corporate capital rather than enacting social change. Care, in its ideal, is opposed to neoliberalism: resisting individuality in favor of community and refusing to reduce humans to capital. Yet, paternalistic care can be a weapon – used to ensnare and to oppress. Through a critical technocultural discourse analysis of platforms’ public utterances and policy changes after the Dobbs leak, we find that platforms redefine care in three main ways. For users, care is neoliberal - platforms provide good privacy options, for which users are individually responsible. For employees, care is paternalistic - employees are offered money for healthcare, at the expense of free expression. Finally, ultimate care is for the platform - that company culture is protected, alliance with the state unthreatened, and above all, profit is promoted. Platform decisions are revealed to extend care in some ways, while also maintaining control over users and their data.

INFLUENCE WITH TRAFFIC RATE AND BEACON TIME FOR JAMMING ATTACK ON 802.11B IN MOBILE AD HOC NETWORKS

MANETs have distinctive characteristics like dynamic topology, wireless radio medium, restricted resources and lack of centralized administration; as a result, they're liable to differing types of attacks in several layers of protocol stack. Every node in a Manet is capable of acting as a router. The need for a secure Manet networks is powerfully tied to the protection and privacy options. This Jamming attacks are one in every of them. These occur by transmittal continuous radio ways in which to inhibit the transmission among sender and receiver. These attacks have an effect on the network by decreasing the network performance with buffer size, beacon amount varied as certained result on mobile ad-hoc network degrade the performance with Jamming attack. This work includes a network with high Mobility IEEE 802.11b with improved AODV (Ad hoc On Demand Distance Vector) routing protocol parameters. FTP with high rate is being generated within the network. For the Simulation purpose we tend to utilized OPNET (Optimized Network Engineering Tool) MODELER 14.5 is employed for simulation. The performance of network is measured with relation to the QoS parameters.

Capturing drivers’ privacy preferences for intelligent transportation systems: An intercultural perspective

While recent research on intelligent transportation systems including vehicular communication systems has focused on technical aspects, little research work has been conducted on drivers’ privacy perceptions and preferences. Understanding the driver’s privacy perceptions and preferences will allow researchers to design usable privacy and identity management systems offering user privacy choices and controls for intelligent transportation systems. We conducted in-depth semi-structured interviews with 17 Swedish drivers to analyse their privacy perceptions and preferences for intelligent transportation systems, particularly for user control and for privacy trade-offs with cost, safety and usability. We also compare our results from the interviews with Swedish drivers with results from interviews that we conducted previously with South African drivers. Our cross-cultural comparison shows that perceived privacy implications, the drivers’ willingness to share location information under certain conditions with other parties, as well as their appreciation of Privacy Enhancing Technologies differ significantly across drivers with different cultural backgrounds. We further discuss the cultural impact on privacy preferences, including those for privacy trade-offs, and the implications of our results for usable privacy-enhancing Identity Management for future vehicular communication systems. In particular, we provide recommendations for suitable pre-defined privacy options to be offered to users with different cultural backgrounds enabling them to easily make privacy-related control choices.

Privacy-Aware Participant Recruitment in Opportunistic Device to Device Networks

In most of the existing mobile applications for data collection and data analytics, either the privacy issue is frequently neglected or the privacy options are not configurable by the participants. This paper proposes configurable privacy level by potential crowdsourcing participants who are able to choose their desirable privacy level and get paid based on the quality of data they provide to the originator in Device to Device network (D2D). Combining with the encryption technique, not only the user’s privacy is protected, but also the encryption complexity is reduced. We first formulate the participant recruitment process into an optimization problem from the participants’ perspective by considering the competition and collaboration among existing and candidate participants in order to achieve the best utility. Then we design a distributed approximate scheme that relies on participants’ local knowledge to complete the overall recruitment task. We implement the approximate approach in Dell Streak tablets and carry out a campus-scale experiment for 21 days, plus run simulations for more extensive and detailed evaluation under various task settings. The results demonstrate the efficiency of the proposed approaches and disclose valuable insights for practical considerations in D2D based crowdsourcing.

Digital forensic analysis methodology for private browsing: Firefox and Chrome on Linux as a case study

The web browser has become one of the basic tools of everyday life. A tool that is increasingly used to manage personal information. This has led to the introduction of new privacy options by the browsers, including private mode. In this paper, a methodology to explore the effectiveness of the private mode included in most browsers is proposed. A browsing session was designed and conducted in Mozilla Firefox and Google Chrome running on four different Linux environments. After analyzing the information written to disk and the information available in memory, it can be observed that Firefox and Chrome did not store any browsing-related information on the hard disk. However, memory analysis reveals that a large amount of information could be retrieved in some of the environments tested. For example, for the case where the browsers were executed in a VMware virtual machine, it was possible to retrieve most of the actions performed, from the keywords entered in a search field to the username and password entered to log in to a website, even after restarting the computer. In contrast, when Firefox was run on a slightly hardened non-virtualized Linux, it was not possible to retrieve any browsing-related artifacts after the browser was closed.

Assessment of Smart Home Assistants as an IoT

A smart home personal assistant technology is an intrinsic system, which incorporates many elements such as users, Smart Home Personal Assistants (SPA) devices, cloud, skill provider and other responsive devices. Even though Smart Home Personal Assistants give a robust security and privacy options, the devices face many weaknesses, which make the system vulnerable and can be comprised by adversaries, who can capitalize on limitations to gain access to delicate information and privacy of users. In this research, the aim is to assess how invention and innovation of security and SPA can be harnessed by users to interrelate with the system. Subsequently, this write-up will address both the problems related to the system and attempt to bring in solutions, which makes the technology more adaptable and versatile to all users. Initial studies show that some of the weakness underlying the technology include the open-nature of the voice channel, complexity of the architecture, software implications, and the utility of the technology to less proficient users. As a result, the study anticipates at solving the voice squatting attack, using the SPA intelligent assistant, incorporating a filer to filter the ultrasonic attack and noise as well as trying to assess the efficacy of the elements developed against the voice squatting attacks. The study found out that there is a need to mitigate the attacks on the blockchain technology and Natural Language Processors (NLP) to assure protection of SPA from attacks.

A case for location based contact tracing.

We present an evaluation of the effectiveness of manual contact tracing compared to bulletin board contact tracing. Classical contact tracing relies on reaching individuals who have been in proximity to an infectious person. A bulletin board approach focuses on identifying locations visited by an infectious person, and then contacting those who were at those locations. We present results comparing their effects on the overall reproductive number as well as the incidence and prevalence of disease. We evaluate them by building a new agent based simulation (ABS) model using the Susceptible Exposed Infectious and Recovered (SEIR) framework for disease spread. We conduct simulation experiments to quantify the effectiveness of these two models of contact tracing by calibrating the model to be compatible with SARS-CoV-2. We find that bulletin board contact tracing gives comparable results in terms of the reproductive number, duration, prevalence and incidence but is less resource intensive, easier to implement and offers a wider range of privacy options. Our experiments show that location-based bulletin board contact tracing can improve manual contact tracing.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10729-021-09567-z.

Collaborative Online Professional Development Design

COVID-19 has impacted not only higher education teaching practice, but also professional development (PD) such as F2F workshop sessions for supervisor training have had to pivot to online modes. This presentation overviews the process of designing an academic supervision training workshop as an online module that can be used as a design framework for other professional development and training contexts. We used a design-based research methodology (McKenney & Reeves, 2019) encompassing three stages.
 Stage 1 Evaluation and Analysis
 We formed a collaborative design team of two academics and a professional staff member and met regularly over Zoom as due to COVID-19 we were all working from home, and across countries (Australia and New Zealand). We began with an evaluation of the existing introductory supervision online module to identify key design elements and refinements that we could integrate into the new refresher module. This was followed by a collaborative definition of the scope of the new PD module that was focused upon providing a ‘Refresher Course’ for experienced higher degree research supervisors. We decided to focus the content of the module upon authentic user-generated scenarios from highly experienced academics across the university. User reflection was designed through an optional link to contribute to an institutional Wiki page to provide tips and comments from user experiences in supervision. The analysis of users needing an informative but short time investment in the refresher module highlighted the need for collating resources that would be referenced throughout the module to be made available at the end of the module as a downloadable interactive PDF resource for users.
 Stage 2 Collaborative Prototype Design
 An initial storyboard module prototype based upon learning object and instructional design principles (Boyle, 2003; Author1, 2007; Reigeluth et al., 2016) was created. While we began with big ideas based upon the rapid prototype of how to make the new module interactive and appealing to the users there were significant design restraints imposed by the institutional Course Builder online development platform, that provides a basic Web 1.0 approach to online module design and delivery. This meant that user interaction options were limited, file size uploads were small, and display dimensions fixed. Therefore, Vimeo was used to host and embed the video vignettes in the module pages through an institutional license providing a range of privacy and customisation options. We were able to negotiate these technical design hurdles, albeit with downgraded expectations of user interaction within our new module design.
 Stage 3 Evaluation and Redesign
 User feedback on the initial prototype was very positive and the user-generated video vignettes and funky iPad created graphics were highlights. Feedback also highlighted the need for additional user instructions, a more unified design across the module and implementation of accessibility features such as closed captioning for the video vignettes. This was achieved through maintaining a consistent font and paragraphing style and creating a new embedded player style format for the Vimeo video host platform. Closed captions were added by manually transcribing and timecoding the videos on Vimeo.

Machine-Learning Implementation in Clinical Anesthesia: Opportunities and Challenges.

Machine-Learning Implementation in Clinical Anesthesia: Opportunities and Challenges.

Asynchronous Volunteer Engagement in Online Continuing Education Using Virtual Communities

The Wisconsin Master Gardener Program team used the Google+ Community platform to provide an engaging online discussion forum for asynchronous continuing education experiences. Applications of such a tool for volunteer online education have numerous benefits, including the capacity for asynchronous posting, ease of posting, privacy options, wide availability, and the potential for internal troubleshooting.

Designing a Platform for Ethical Citizen Science: A Case Study of CitSci.org

Involving the public in scientific discovery offers opportunities for engagement, learning, participation, and action. Since its launch in 2007, the CitSci.org platform has supported hundreds of community-driven citizen science projects involving thousands of participants who have generated close to a million scientific measurements around the world. Members using CitSci.org follow their curiosities and concerns to develop, lead, or simply participate in research projects. While professional scientists are trained to make ethical determinations related to the collection of, access to, and use of information, citizen scientists and practitioners may be less aware of such issues and more likely to become involved in ethical dilemmas. In this era of big and open data, where data sharing is encouraged and open science is promoted, privacy and openness considerations can often be overlooked. Platforms that support the collection, use, and sharing of data and personal information need to consider their responsibility to protect the rights to and ownership of data, the provision of protection options for data and members, and at the same time provide options for openness. This requires critically considering both intended and unintended consequences of the use of platforms, data, and volunteer information. Here, we use our journey developing CitSci.org to argue that incorporating customization into platforms through flexible design options for project managers shifts the decision-making from top-down to bottom-up and allows project design to be more responsive to goals. To protect both people and data, we developed—and continue to improve—options that support various levels of “open” and “closed” access permissions for data and membership participation. These options support diverse governance styles that are responsive to data uses, traditional and indigenous knowledge sensitivities, intellectual property rights, personally identifiable information concerns, volunteer preferences, and sensitive data protections. We present a typology for citizen science openness choices, their ethical considerations, and strategies that we are actively putting into practice to expand privacy options and governance models based on the unique needs of individual projects using our platform.

A Content Analysis of the Privacy Policies of Cloud Computing Services

ABSTRACTCloud computing is increasingly popular across all sectors and offers users unparalleled scalability, elasticity, and flexibility. However, the rapid transition toward cloud computing has raised privacy and confidentiality concerns. Cloud service providers can access users' data, and private information may be accidentally or deliberately disclosed or used for unauthorized purposes. Privacy policies are intended to provide users with information about privacy practices and their privacy options. This study performs content analysis of the privacy policies of 47 cloud service providers who offer services to business users. This analysis identifies what information is collected and why, to whom the information is disclosed, and what measures are in place for data security, data retention, and data complaints. Additionally, we investigate the readability, uncertainty language, and linguistic tone of cloud service privacy policies. Our results offer implications for cloud service users, providers, and policymakers, and provide directions for future research.Data Availability: Data are available upon request.

Toward Automated Online Photo Privacy

Online photo sharing is an increasingly popular activity for Internet users. More and more users are now constantly sharing their images in various social media, from social networking sites to online communities, blogs, and content sharing sites. In this article, we present an extensive study exploring privacy and sharing needs of users’ uploaded images. We develop learning models to estimate adequate privacy settings for newly uploaded images, based on carefully selected image-specific features. Our study investigates both visual and textual features of images for privacy classification. We consider both basic image-specific features, commonly used for image processing, as well as more sophisticated and abstract visual features. Additionally, we include a visual representation of the sentiment evoked by images. To our knowledge, sentiment has never been used in the context of image classification for privacy purposes. We identify the smallest set of features, that by themselves or combined together with others, can perform well in properly predicting the degree of sensitivity of users’ images. We consider both the case of binary privacy settings (i.e., public, private), as well as the case of more complex privacy options, characterized by multiple sharing options. Our results show that with few carefully selected features, one may achieve high accuracy, especially when high-quality tags are available.

The Internet Use for Health Information Seeking among Ghanaian University Students: A Cross-Sectional Study.

The aim of the study was to investigate university students' use of the Internet for health purpose in the Ghanaian context. The study employed a quantitative cross-sectional design. A total of 650 out of 740 students selected from 3 different universities participated, giving a response rate of 87.7% (650/740). Data were obtained using questionnaires and frequency and percentages were used to analyze data. The results show that university students are active users of the Internet as 78.3% (509/650) used Internet daily and 67.7% (440/650) use Internet for health purposes, for reasons including availability and ease of accessing information, privacy, confidentiality, and affordability. Use of Internet was constrained by unreliable and slow connection, high cost of Internet, and unreliable power supply. Also, 72.4% (315/435) used the online health information obtained as a basis for lifestyle change and only 39.5% (170/430) consulted health professionals after obtaining online information. The study concludes that students use Internet to seek online health support. The use of Internet to communicate with young people in relation to their health must therefore be explored. There is the need to be aware of online safety issues for young adults, including the need to provide information on privacy options.

Big data analytics and the limits of privacy self-management

This article looks at how the logic of big data analytics, which promotes an aura of unchallenged objectivity to the algorithmic analysis of quantitative data, preempts individuals’ ability to self-define and closes off any opportunity for those inferences to be challenged or resisted. We argue that the predominant privacy protection regimes based on the privacy self-management framework of “notice and choice” not only fail to protect individual privacy, but also underplay privacy as a collective good. To illustrate this claim, we discuss how two possible individual strategies—withdrawal from the market (avoidance) and complete reliance on market-provided privacy protections (assimilation)—may result in less privacy options available to the society at large. We conclude by discussing how acknowledging the collective dimension of privacy could provide more meaningful alternatives for privacy protection.

Exploration of Influencing Factors that Effecting Facebook Privacy Awareness on Bangladeshi Undergraduate University Students

Our social technologies are increasingly serving as an obstacle to young people in the way that, the users of online social networking communities are disclosing large amounts of personal information and putting themselves at a variety of risks . While social media connect us to more digital relationships, at the same time, they deteriorate our ability to maintain healthy relationships in real life. User should familiarize themselves with sites' privacy options; its implications for their privacy settings and should make sure to understand who could access their profiles, and how that information could be used, whether information could end up in the hands of marketing companies, government or criminal investigation agencies, Political Campaign and employers have been known to visit such sites to check personal details about prospective workers. These bits and pieces of our personal identities could potentially have reputational harm. In fact, problem is not using the technology. The problem is using it unconsciously. Recently Bangladesh faced communal fracas due to unconscious religious posting on Facebook .On the top of that, political party are using facebook as a channel of their campaign which may create social violence. This scenery might be same to other country as if more than 1000 million users are using it. Author investigated to explore the factors relating privacy awareness on Bangladeshi undergraduate students to focus actual influencing factors and aggregated relationship with Awareness of Facebook Privacy. From our findings we concluded that, Bangladeshi students do care about their privacy on Facebook, and a large number of them are now making regular changes to their settings.

Leveraging Personalization to Facilitate Privacy

Online social networks have enabled new methods and modalities of collaboration and sharing. These advances bring privacy concerns: online social data is more accessible and persistent and simultaneously less contextualized than traditional social interactions. To allay these concerns, many web services allow users to configure their privacy settings based on a set of multiple-choice questions. We suggest a new paradigm for privacy options. Instead of suggesting the same defaults to each user, services can leverage knowledge of users' traits to recommend a machine-learned prediction of their privacy preferences for Facebook. As a case study, we build and evaluate MyPrivacy, a publicly available web application that suggests personalized privacy settings. An evaluation with 199 users shows that users find the suggestions to be appropriate and private; furthermore, they express intent to implement the recommendations made by MyPrivacy. This supports the proposal to put personalization to work in online communities to promote privacy and security.

YourPrivacyProtector: A Recommender System for Privacy Settings in Social Networks

Ensuring privacy of users of social networks is probably an unsolvable conundrum. At the same time, an informed use of the existing privacy options by the social network participants may alleviate - or even prevent - some of the more drastic privacy-averse incidents. Unfortunately, recent surveys show that an average user is either not aware of these options or does not use them, probably due to their perceived complexity. It is therefore reasonable to believe that tools assisting users with two tasks: 1) understanding their social net behavior in terms of their privacy settings and broad privacy categories, and 2)recommending reasonable privacy options, will be a valuable tool for everyday privacy practice in a social network context. This paper presents YourPrivacyProtector, a recommender system that shows how simple machine learning techniques may provide useful assistance in these two tasks to Facebook users. We support our claim with empirical results of application of YourPrivacyProtector to two groups of Facebook users.

Consumer Privacy Enforcement in Context-Aware Web Services

Privacy protection constitutes a genuine human right reflected both in the legislation and in different aspects of software engineering. Sensitive information needs to be protected in end-users interaction with Web Services especially in cases, where context-aware features are included. In this work the authors address the inclusion of consumer privacy preferences in the provision of context-aware Web Services. To achieve this the authors propose, on the one hand, a preferences language, where end-users can specify their privacy options, namely Consumer Privacy Language, and, on the other hand, a seamless enforcement mechanism that considers consumer preferences by intercepting and modifying appropriately Simple Object Access Protocol request and response messages. The enforcement approach has been evaluated based on various execution metrics for an example use case consisting of various Web Services and for different users configurations demonstrating the usefulness of the approach assisting towards the provision of privacy-aware environments.

Context-Dependent Expectations of Privacy in Self-Generated Mobile Health Data

Converging technological, regulatory, and social forces point to a future of health care delivery that will emphasize personalized information generation, open access, and collaborative decision-making. One driver of this transition is the increasing availability of health apps, digital sensors, and other health self monitoring tools that enable individuals to track a broad swath of their own physiological and behavioral health in near-real-time and selectively share that information with an expanding team of supportive clinical care providers, family, friends, and public health experts. Because self-generated health data are subject to few statutory privacy protections, users’ privacy and security rest heavily on their own data management practices, which in turn rest on a number of complex factors, including individual conceptualizations of tracking devices, device wearing habits, and understandings of information flows and privacy rights.The broad goal of this exploratory research was to investigate how users of health self-tracking services engage with and understand the wider context of the health self-tracking ecosystem. We were particularly interested in understanding how individuals understand and utilize their privacy options to manage online health information.