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"All apps do this": Comparing Privacy Concerns Towards Privacy Tools and Non-Privacy Tools for Social Media Content
Abstract
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 non-privacy 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.
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When requesting a web-based service, users often fail in setting the website's privacy settings according to their self privacy preferences. Being overwhelmed by the choice of preferences, a lack of knowledge of related technologies or unawareness of the own privacy preferences are just some reasons why users tend to struggle. To address all these problems, privacy setting prediction tools are particularly well suited. Such tools aim to lower the burden to set privacy preferences according to owners privacy preferences. To be in line with the increased demand for explainability and interpretability by regulatory obligations - such as the General Data Protection Regulation (GDPR) in Europe - this paper introduces an explainable model for default privacy setting prediction. Compared to the previous work we present an improved feature selection, increased interpretability of each step in model design and enhanced evaluation metrics to better identify weaknesses in the model's design before they go into production. As a result, we aim to provide an explainable and transparent tool for default privacy setting prediction which users easily understand and are therefore more likely to use.
Privacy and security tools can help users protect themselves online. Unfortunately, people are often unaware of such tools, and have potentially harmful misconceptions about the protections provided by the tools they know about. Effectively encouraging the adoption of privacy tools requires insights into people’s tool awareness and understanding. Towards that end, we conducted a demographically-stratified survey of 500 US participants to measure their use of and perceptions about five web browsing-related tools: private browsing, VPNs, Tor Browser, ad blockers, and antivirus software. We asked about participants’ perceptions of the protections provided by these tools across twelve realistic scenarios. Our thematic analysis of participants’ responses revealed diverse forms of misconceptions. Some types of misconceptions were common across tools and scenarios, while others were associated with particular combinations of tools and scenarios. For example, some participants suggested that the privacy protections offered by private browsing, VPNs, and Tor Browser would also protect them from security threats – a misconception that might expose them to preventable risks. We anticipate that our findings will help researchers, tool designers, and privacy advocates educate the public about privacy- and security-enhancing technologies.
The steady reports of privacy invasions online paints a picture of the Internet growing into a more dangerous place. This is supported by reports of the potential scale for online harms facilitated by the mass deployment of online technology and the data-intensive web. While Internet users often express concern about privacy, some report taking actions to protect their privacy online. We investigate the methods and technologies that individuals employ to protect their privacy online. We conduct two studies, of N=180 and N=907, to elicit individuals' use of privacy methods online, within the US, the UK and Germany. We find that non-technology methods are among the most used methods in the three countries. We identify distinct groupings of privacy methods usage in a cluster map. The map shows that together with non-technology methods of privacy protection, simple PETs that are integrated in services, form the most used cluster, whereas more advanced PETs form a different, least used cluster. We further investigate user perception and reasoning for mostly using one set of PETs in a third study with N=183 participants. We do not find a difference in perceived competency in protecting privacy online between advanced and simpler PETs users. We compare use perceptions between advanced and simpler PETs and report on user reasoning for not using advanced PETs, as well as support needed for potential use. This paper contributes to privacy research by eliciting use and perception of use across 43 privacy methods, including 26 PETs across three countries and provides a map of PETs usage. The cluster map provides a systematic and reliable point of reference for future user-centric investigations across PETs. Overall, this research provides a broad understanding of use and perceptions across a collection of PETs, and can lead to future research for scaling use of PETs.
Federated learning (FL) is a privacy-preserving technique for training a vast amount of decentralized data and making inferences on mobile devices. As a typical language modeling problem, mobile keyboard prediction aims at suggesting a probable next word or phrase and facilitating the human-machine interaction in a virtual keyboard of the smartphone or laptop. Mobile keyboard prediction with FL hopes to satisfy the growing demand that high-level data privacy be preserved in artificial intelligence applications even with the distributed models training. However, there are two major problems in the federated optimization for the prediction: (1) aggregating model parameters on the server-side and (2) reducing communication costs caused by model weights collection. To address the above issues, traditional FL methods simply use averaging aggregation or ignore communication costs. We propose a novel Federated Mediation (FedMed) framework with the adaptive aggregation, mediation incentive scheme, and topK strategy to address the model aggregation and communication costs. The performance is evaluated in terms of perplexity and communication rounds. Experiments are conducted on three datasets (i.e., Penn Treebank, WikiText-2, and Yelp) and the results demonstrate that our FedMed framework achieves robust performance and outperforms baseline approaches.
Photographs taken in public places often contain bystanders-people who are not the main subject of a photo. These photos, when shared online, can reach a large number of viewers and potentially undermine the bystanders' privacy. Furthermore , recent developments in computer vision and machine learning can be used by online platforms to identify and track individuals. To combat this problem, researchers have proposed technical solutions that require bystanders to be proactive and use specific devices or applications to broadcast their privacy policy and identifying information to locate them in an image. We explore the prospect of a different approach-identifying bystanders solely based on the visual information present in an image. Through an online user study, we catalog the rationale humans use to classify subjects and bystanders in an image, and systematically validate a set of intuitive concepts (such as intentionally posing for a photo) that can be used to automatically identify bystanders. Using image data, we infer those concepts and then use them to train several classifier models. We extensively evaluate the models and compare them with human raters. On our initial dataset, with a 10-fold cross validation, our best model achieves a mean detection accuracy of 93% for images when human raters have 100% agreement on the class label and 80% when the agreement is only 67%. We validate this model on a completely different dataset and achieve similar results, demonstrating that our model generalizes well.
Today’s environment of data-driven business models relies heavily on collecting as much personal data as possible. Besides being protected by governmental regulation, internet users can also try to protect their privacy on an individual basis. One of the most famous ways to accomplish this, is to use privacy-enhancing technologies (PETs). However, the number of users is particularly important for the anonymity set of the service. The more users use the service, the more difficult it will be to trace an individual user. There is a lot of research determining the technical properties of PETs like Tor or JonDonym, but the use behavior of the users is rarely considered, although it is a decisive factor for the acceptance of a PET. Therefore, it is an important driver for increasing the user base.
We undertake a first step towards understanding the use behavior of PETs employing a mixed-method approach. We conducted an online survey with 265 users of the anonymity services Tor and JonDonym (124 users of Tor and 141 users of JonDonym). We use the technology acceptance model as a theoretical starting point and extend it with the constructs perceived anonymity and trust in the service in order to take account for the specific nature of PETs. Our model explains almost half of the variance of the behavioral intention to use the two PETs. The results indicate that both newly added variables are highly relevant factors in the path model. We augment these insights with a qualitative analysis of answers to open questions about the users’ concerns, the circumstances under which they would pay money and choose a paid premium tariff (only for JonDonym), features they would like to have and why they would or would not recommend Tor/JonDonym. Thereby, we provide additional insights about the users’ attitudes and perceptions of the services and propose new use factors not covered by our model for future research.
With the growing popularity of online social networks, a large amount of private or sensitive information has been posted online. In particular, studies show that users sometimes reveal too much information or unintentionally release regretful messages, especially when they are careless, emotional, or unaware of privacy risks. As such, there exist great needs to be able to identify potentially-sensitive online contents, so that users could be alerted with such findings. In this paper, we propose a context-aware, text-based quantitative model for private information assessment, namely PrivScore , which is expected to serve as the foundation of a privacy leakage alerting mechanism. We first solicit diverse opinions on the sensitiveness of private information from crowdsourcing workers, and examine the responses to discover a perceptual model behind the consensuses and disagreements. We then develop a computational scheme using deep neural networks to compute a context-free PrivScore (i.e., the “consensus” privacy score among average users). Finally, we integrate tweet histories, topic preferences and social contexts to generate a personalized context-aware PrivScore. This privacy scoring mechanism could be employed to identify potentially-private messages and alert users to think again before posting them to OSNs.
Due to an increasing collection of personal data by internet companies and several data breaches, research related to privacy gained importance in the last years in the information systems domain. Privacy concerns can strongly influence users' decision to use a service. The Internet Users Information Privacy Concerns (IUIPC) construct is one operationalization to measure the impact of privacy concerns on the use of technologies. However, when applied to a privacy enhancing technology (PET) such as an anonymization service the original rationales do not hold anymore. In particular, an inverted impact of trusting and risk beliefs on behavioral intentions can be expected. We show that the IUIPC model needs to be adapted for the case of PETs. In addition, we extend the original causal model by including trusting beliefs in the anonymization service itself as well as a measure for privacy literacy. A survey among 124 users of the anonymization service Tor shows that trust in Tor has a statistically significant effect on the actual use behavior of the PET. In addition, the results indicate that privacy literacy has a negative impact on trusting beliefs in general and a positive effect on trust in Tor.
Today’s environment of data-driven business models relies heavily on collecting as much personal data as possible. One way to prevent this extensive collection, is to use privacy-enhancing technologies (PETs). However, until now, PETs did not succeed in larger consumer markets. In addition, there is a lot of research determining the technical properties of PETs, i.e. for Tor, but the use behavior of the users and, especially, their attitude towards spending money for such services is rarely considered. Yet, determining factors which lead to an increased willingness to pay (WTP) for privacy is an important step to establish economically sustainable PETs. We argue that the lack of WTP for privacy is one of the most important reasons for the non-existence of large players engaging in the offering of a PET. The relative success of services like Tor corroborates this claim since this is a service without any monetary costs attached. Thus, we empirically investigate the drivers of active users’ WTP of a commercial PET - JonDonym - and compare them with the respective results for a donation-based service - Tor. Furthermore, we provide recommendations for the design of tariff schemes for commercial PETs.
Pervasive photo sharing in online social media platforms can cause unintended privacy violations when elements of an image reveal sensitive information. Prior studies have identified image obfuscation methods (e.g., blurring) to enhance privacy, but many of these methods adversely affect viewers' satisfaction with the photo, which may cause people to avoid using them. In this paper, we study the novel hypothesis that it may be possible to restore viewers' satisfaction by 'boosting' or enhancing the aesthetics of an obscured image, thereby compensating for the negative effects of a privacy transform. Using a between-subjects online experiment, we studied the effects of three artistic transformations on images that had objects obscured using three popular obfuscation methods validated by prior research. Our findings suggest that using artistic transformations can mitigate some negative effects of obfuscation methods, but more exploration is needed to retain viewer satisfaction.
Due to an increasing collection of personal data by internet companies and several data breaches, research related to privacy gained importance in the last years in the information systems domain. Privacy concerns can strongly influence users’ decision to use a service. The Internet Users Information Privacy Concerns (IUIPC) construct is one operationalization to measure the impact of privacy concerns on the use of technologies. However, when applied to a privacy enhancing technology (PET) such as an anonymization service the original rationales do not hold anymore. In particular, an inverted impact of trusting and risk beliefs on behavioral intentions can be expected. We show that the IUIPC model needs to be adapted for the case of PETs. In addition, we extend the original causal model by including trust beliefs in the anonymization service itself. A survey among 124 users of the anonymization service Tor shows that they have a significant effect on the actual use behavior of the PET.
Today's environment of data-driven business models relies heavily on collecting as much personal data as possible. This is one of the main causes for the importance of privacy-enhancing technologies (PETs) to protect internet users' privacy. Still, PETs are rather a niche product used by relatively few users on the internet. We undertake a first step towards understanding the use behavior of such technologies. For that purpose, we conducted an online survey with 141 users of the anonymity service "JonDonym". We use the technology acceptance model as a theoretical starting point and extend it with the constructs perceived anonymity and trust in the service. Our model explains almost half of the variance of the behavioral intention to use JonDonym and the actual use behavior. In addition, the results indicate that both added variables are highly relevant factors in the path model.
With the rise of digital photography and social networking, people are sharing personal photos online at an unprecedented rate. In addition to their main subject matter, photographs often capture various incidental information that could harm people's privacy. While blurring and other image filters may help obscure private content, they also often affect the utility and aesthetics of the photos, which is important since images shared in social media are mainly for human consumption. Existing studies of privacy-enhancing image filters either primarily focus on obscuring faces, or do not systematically study how filters affect image utility. To understand the trade-offs when obscuring various sensitive aspects of images, we study eleven filters applied to obfuscate twenty different objects and attributes, and evaluate how effectively they protect privacy and preserve image quality for human viewers.
Anonymity services have seen high growth rates with increased usage in the past few years. Among various services, Tor is one of the most popular peer-to-peer anonymizing service. In this survey paper, we summarize, analyze, classify and quantify 26 years of research on the Tor network. Our research shows that `security' and `anonymity' are the most frequent keywords associated with Tor research studies. Quantitative analysis shows that the majority of research studies on Tor focus on `deanonymization' the design of a breaching strategy. The second most frequent topic is analysis of path selection algorithms to select more resilient paths. Analysis shows that the majority of experimental studies derived their results by deploying private testbeds while others performed simulations by developing custom simulators. No consistent parameters have been used for Tor performance analysis. The majority of authors performed throughput and latency analysis.
Despite advances in technology being a driver of paramedic professional development, particularly over the past decade, the introduction of new forms of technology appears to have presented paramedics with some professional challenges. Paramedics, pre-hospital clinicians, and ambulance service providers in both the United Kingdom and Australia, have begun using social media technology to communicate what they do to the general public. Unfortunately some of the material that has been communicated appears to breach professional standards of practice, and therefore has the potential to cause harm to the patient, the individual paramedic, and the paramedic profession more broadly. This article will present the rationale behind why this behaviour is unprofessional, ethically and legally unsound, and why it must cease. We offer a tool that will assist paramedics, and other healthcare professionals, to practise safe and professional social media use in their workplace.
Privacy in image and video data has become an important subject since cameras are being installed in an increasing number of public and private spaces. Specifically, in assisted living, intelligent monitoring based on computer vision can allow one to provide risk detection and support services that increase people's autonomy at home. In the present work, a level-based visualisation scheme is proposed to provide visual privacy when human intervention is necessary, such as at telerehabilitation and safety assessment applications. Visualisation levels are dynamically selected based on the previously modelled context. In this way, different levels of protection can be provided, maintaining the necessary intelligibility required for the applications. Furthermore, a case study of a living room, where a top-view camera is installed, is presented. Finally, the performed survey-based evaluation indicates the degree of protection provided by the different visualisation models, as well as the personal privacy preferences and valuations of the users.
While online social networking is a popular way for people to share information, it carries the risk of unintentionally disclosing personal information. One way to reduce this risk is to anonymize personal information in messages before they are posted. Furthermore, if personal information is somehow disclosed, the person who disclosed it should be identifiable. Several methods developed for anonymizing personal information in natural language text simply remove sensitive phrases, making the anonymized text message unnatural. Other methods change the message by using synonymization or structural alteration to create fingerprints for detecting disclosure, but they do not support the creation of a sufficient number of fingerprints for friends of an online social network user. We have developed a system for anonymizing personal information in text messages that generalizes sensitive phrases. It also creates a sufficient number of fingerprints of a message by using synonyms so that, if personal information is revealed online, the person who revealed it can be identified. A distribution metric is used to ensure that the degree of anonymization is appropriate for each group of friends. A threshold is used to improve the naturalness of the fingerprinted messages so that they do not catch the attention of attackers. Evaluation using about 55,000 personal tweets in English demonstrated that our system creates sufficiently natural fingerprinted messages for friends and groups of friends. The practicality of the system was demonstrated by creating a web application for controlling messages posted on Facebook.
Many users welcome personalized services, but are reluctant to provide the information about themselves that personalization requires. Performing personalization exclusively at the client side (e.g., on one's smartphone) may conceptually increase privacy, because no data is sent to a remote provider. But does client-side personalization (CSP) also increase users' perception of privacy?
We developed a causal model of privacy attitudes and behavior in personalization, and validated it in an experiment that contrasted CSP with personalization at three remote providers: Amazon, a fictitious company, and the "Cloud". Participants gave roughly the same amount of personal data and tracking permissions in all four conditions. A structural equation modeling analysis reveals the reasons: CSP raises the fewest privacy concerns, but does not lead in terms of perceived protection nor in resulting self-anticipated satisfaction and thus privacy-related behavior. Encouragingly, we found that adding certain security features to CSP is likely to raise its perceived protection significantly. Our model predicts that CSP will then also sharply improve on all other privacy measures.
Homomorphic encryption is the encryption scheme which means the operations on the encrypted data. Homomorphic encryption can be applied in any system by using various public key algorithms. When the data is transferred to the public area, there are many encryption algorithms to secure the operations and the storage of the data. But to process data located on remote server and to preserve privacy, homomorphic encryption is useful that allows the operations on the cipher text, which can provide the same results after calculations as the working directly on the raw data. In this paper, the main focus is on public key cryptographic algorithms based on homomorphic encryption scheme for preserving security. The case study on various principles and properties of homomorphic encryption is given and then various homomorphic algorithms using asymmetric key systems such as RSA, ElGamal, Paillier algorithms as well as various homomorphic encryption schemes such as Brakerski-Gentry-Vaikuntanathan (BGV), Enhanced homomorphic Cryptosystem (EHC), Algebra homomorphic encryption scheme based on updated ElGamal (AHEE), Non-interactive exponential homomorphic encryption scheme (NEHE) are investigated.
The real value of mobile applications is heavily dependent on consumers' trust in the privacy of their personal information and location data. However, research has generated few results based on actual information disclosure and even less that is based on longitudinal behavior. The purpose of this study is to execute a unique and authentic field experiment involving real risks and consumer behaviors regarding information disclosure over mobile devices. We compare two theoretical explanations of disclosure decisions: privacy calculus and prospect theory. Our results indicate that consumers are best modeled as "bounded" rational actors concerning their disclosure behavior. Also, actual information disclosure behavior over mobile applications is a more multifaceted issue than research has treated it thus far. For practice, mobile application providers should be aware that increasing the benefits of information disclosure via the app may have the counterintuitive effect of increasing perceived risk and reducing consumer disclosure.
Many research designs require the assessment of inter-rater reliability (IRR) to demonstrate consistency among observational ratings provided by multiple coders. However, many studies use incorrect statistical procedures, fail to fully report the information necessary to interpret their results, or do not address how IRR affects the power of their subsequent analyses for hypothesis testing. This paper provides an overview of methodological issues related to the assessment of IRR with a focus on study design, selection of appropriate statistics, and the computation, interpretation, and reporting of some commonly-used IRR statistics. Computational examples include SPSS and R syntax for computing Cohen's kappa and intra-class correlations to assess IRR. The assessment of inter-rater reliability (IRR, also called inter-rater agreement) is often necessary for research designs where data are collected through ratings provided by trained or untrained coders. However, many studies use incorrect statistical analyses to compute IRR, misinterpret the results from IRR analyses, or fail to consider the implications that IRR estimates have on statistical power for subsequent analyses. This paper will provide an overview of methodological issues related to the assessment of IRR, including aspects of study design, selection and computation of appropriate IRR statistics, and interpreting and reporting results. Computational examples include SPSS and R syntax for computing Cohen's kappa for nominal variables and intra-class correlations (ICCs) for ordinal, interval, and ratio variables. Although it is beyond the scope of the current paper to provide a comprehensive review of the many IRR statistics that are available, references will be provided to other IRR statistics suitable for designs not covered in this tutorial.
We investigate regrets associated with users' posts on a popular social networking site. Our findings are based on a series of in-terviews, user diaries, and online surveys involving 569 Ameri-can Facebook users. Their regrets revolved around sensitive top-ics, content with strong sentiment, lies, and secrets. Our research reveals several possible causes of why users make posts that they later regret: (1) they want to be perceived in favorable ways, (2) they do not think about their reason for posting or the consequences of their posts, (3) they misjudge the culture and norms within their social circles, (4) they are in a "hot" state of high emotion when posting, or under the influence of drugs or alcohol, (5) their post-ings are seen by an unintended audience, (6) they do not foresee how their posts could be perceived by people within their intended audience, and (7) they misunderstand or misuse the Facebook plat-form. Some reported incidents had serious repercussions, such as breaking up relationships or job losses. We discuss methodologi-cal considerations in studying negative experiences associated with social networking posts, as well as ways of helping users of social networking sites avoid such regrets.
A general inductive approach for analysis of qualitative evaluation data is described. The purposes for using an inductive approach are to (a) condense raw textual data into a brief, summary format; (b) establish clear links between the evaluation or research objectives and the summary findings derived from the raw data; and (c) develop a framework of the underlying structure of experiences or processes that are evident in the raw data. The general inductive approach provides an easily used and systematic set of procedures for analyzing qualitative data that can produce reliable and valid findings. Although the general inductive approach is not as strong as some other analytic strategies for theory or model development, it does provide a simple, straightforward approach for deriving findings in the context of focused evaluation questions. Many evaluators are likely to find using a general inductive approach less complicated than using other approaches to qualitative data analysis.
The German Corona-Warn-App (CWA) is one of the most controversial tools to mitigate the Corona virus spread with roughly 25 million users. In this study, we investigate individuals’ knowledge about the CWA and associated privacy concerns alongside different demographic factors. For that purpose, we conducted a study with 1752 participants in Germany to investigate knowledge and privacy concerns of users and non-users of the German CWA. We investigate the relationship between knowledge and privacy concerns and analyze the demographic effects on both.
Privacy sensitive information (PSI) detection tools have the potential to help users protect their privacy when posting information online, i. e. they can identify when a social media post contains information that users could later regret sharing. However, although users consider this type of tools useful, previous research indicates that the intention of using them is not very high. In this paper, we conduct a user survey (n=147) to investigate the factors that influence the intention to use a PSI detection tool. The results of a logistic regression analysis indicate a positive association of intention to use a PSI detection tool with performance expectation, social influence, and perception of accuracy of the tool. In addition, intention is negatively associated with privacy concerns related to the tool itself and with the participants' self-perceived ability to protect their own privacy. On the other hand, we did not find significant association with the participants' demographic characteristics or social media posting experience. We discuss these findings in the context of the design and development of PSI detection tools.
As the number of data leak scandals and data infringements increase by the day, people are becoming more concerned than ever about their online security. As a result, software and applications designed to reduce, eliminate, or prevent unauthorized processing of users' online personal data, referred to as privacy enhancing technologies (PETs) are gaining momentum. Yet, research investigating what drives users' intention to adopt these technologies is still scant. Drawing on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), this study develops a research framework and tests it with a research design combining structural equation modelling and multi-group analysis. As participants, we recruited 198 members of four online communities where discussion of PETs takes place. Besides confirming the UTAUT2 variables' predictive power on the intention to use PETs, the results of applying the baseline model also provide interesting insight on the mediating role UTAUT2 core constructs play in the relationship between security concerns and intention to adopt PETs. The multi-group analysis, in contrast, revealed that the underlying mechanisms of the theoretical framework we tested work differently when the users' level of expertise is taken into account. This work concludes with managerial implications addressed to both PET providers and any business dealing with online consumer data.
This paper provides the survey materials used to collect the data for the conceptual replication of the Internet Users' Information Privacy Concerns (IUIPC) model by Malhotra et al. (2004). The replication paper (Pape et al., 2020) used awareness, collection and control as constructs for the second order construct of IUIPC, as well as risk and trusting beliefs from the original paper. Instead of intended behavior the self-developed construct of willingness to share was used. Altogether more than 9,000 data points were collected. This paper provides additional materials and details on the participants, and the Japanese survey questions along with an English version for readers who are unfamiliar with Japanese. We hope that the additional information and in particular the Japanese questions provide some background on our study which will allow others a better understanding of our research and to make use of the questions themselves.
To expand the understanding of privacy concerns in the digital sphere, this paper makes use of the Internet Users' Information Privacy Concerns (IUIPC) model by Malhotra et al. (2004). The lack of empirical studies conducted in East-Asian societies makes it difficult, if not impossible, to shed light on multi-cultural differences in information privacy concerns of internet users. Therefore, we collected data of more than 9,000 Japanese respondents to conduct a conceptual replication of the IUIPC model. For our research goal, we reassess the validity and reliability of the IUIPC model for Japan and compare the results with internet users' privacy concerns in the USA. Our results indicate that the second-order IUIPC construct, measured reflectively through the constructs awareness, collection, and control, is reliable and valid. Furthermore, three out of the five structural paths of the IUIPC model were confirmed for our Japanese sample. In contrast to the original study, the impact of IUIPC on trusting beliefs, as well as that of trusting beliefs on risk beliefs was negligible. Statistically significant differences in the IUIPC could only be found for the covariate gender.
The web has improved our life and has provided us with more opportunities to access information and do business. Nonetheless, due to the prevalence of trackers on websites, web users might be subject to profiling while accessing the web, which impairs their online privacy. Privacy browser add-ons, such as DuckDuckGo Privacy Essentials, Ghostery and Privacy Badger, extend the privacy protection that the browsers offer by default, by identifying and blocking trackers. However, the work that focuses on the usability of the privacy add-ons, as well as the users’ awareness, feelings, and thoughts towards them, is rather limited. In this work, we conducted usability evaluations by utilising System Usability Scale and Think-Aloud Protocol on three popular privacy add-ons, i.e., DuckDuckGo Privacy Essentials, Ghostery and Privacy Badger. Our work also provides insights into the users’ awareness of online privacy and attitudes towards the abovementioned privacy add-ons; in particular trust, concern, and control. Our results suggest that the participants feel safer and trusting of their respective add-on. It also uncovers areas for add-on improvement, such as a more visible toolbar logo that offers visual feedback, easy access to thorough help resources, and detailed information on the trackers that have been found.
The strength of an anonymity system depends on the number of users. Therefore, User eXperience (UX) and usability of these systems is of critical importance for boosting adoption and use. To this end, we carried out a study with 19 non-expert participants to investigate how users experience routine Web browsing via the Tor Browser, focusing particularly on encountered problems and frustrations. Using a mixed-methods quantitative and qualitative approach to study one week of naturalistic use of the Tor Browser, we uncovered a variety of UX issues, such as broken Web sites, latency, lack of common browsing conveniences, differential treatment of Tor traffic, incorrect geolocation, operational opacity, etc. We applied this insight to suggest a number of UX improvements that could mitigate the issues and reduce user frustration when using the Tor Browser.
Through a controlled online experiment with 447 Android phone users using their own devices, we investigated how empowering users with information-disclosure control and enhancing their ads awareness affect their installation behaviors, information disclosure, and privacy perceptions toward different mobile apps. In the 3 (control: no, low, high) x 2 (ads awareness: absent, present) x 3 (app context: Wallpaper, BusTracker, Flashlight) fractional factorial between-subjects experiment, we designed privacy notice dialogs that simulate real Android app pre-installation privacy-setting interfaces to implement and manipulate control and ads awareness. Our findings suggest that empowering users with control over information disclosure and enhancing their ads awareness before installation effectively help them make better privacy decisions, increase their likelihood of installing an app, and improve their perceptions of the app. Implications for designing mobile apps' privacy notice dialogs and potential separate-ads-control solutions are discussed.
The evolution of mobile network technologies and smartphones has provided mobile consumers with unprecedented access to Internet and value-added services while on the move. Privacy issues in such context become critically important because vendors may access a large volume of personal information. Although several pioneering studies have examined general privacy risks, few systematic attempts have been made to provide a theory-driven framework on the specific nature of privacy concerns among mobile consumers. To fill the gap in the literature, this article introduced a 9-item scale, which was shown to reasonably represent the dimensionality of mobile users' information privacy concerns (MUIPC), categorized as perceived surveillance, perceived intrusion, and secondary use of personal information. Through a survey study (n=310), the three-factor structure of MUIPC as revealed in exploratory factor analysis was further confirmed through confirmatory factor analysis. Further analysis revealed that the second-order model of MUIPC performed better than its first-order model.
We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in understanding an object's precise 2D location. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old along with per-instance segmentation masks. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.
Today's smartphone applications expect users to make decisions about what information they are willing to share, but fail to provide sufficient feedback about which privacy-sensitive information is leaving the phone, as well as how frequently and with which entities it is being shared. Such feedback can improve users' understanding of potential privacy leakages through apps that collect information about them in an unexpected way. Through a qualitative lab study with 19 participants, we first discuss misconceptions that smartphone users currently have with respect to two popular game applications that frequently collect the phone's current location and share it with multiple third parties. To measure the gap between users' understanding and actual privacy leakages, we use two types of interfaces that we developed: just-in-time notifications that appear the moment data is shared and a visualization that summarizes the shared data. We then report on participants' perceived benefits and concerns regarding data sharing with smartphone applications after experiencing notifications and having viewed the visualization. We conclude with a discussion on how heightened awareness of users and usable controls can mitigate some of these concerns.
We present the results of an online survey of 1,221 Twitter users, comparing messages individuals regretted either saying during in-person conversations or posting on Twitter. Participants generally reported similar types of regrets in person and on Twitter. In particular, they often regretted messages that were critical of others. However, regretted messages that were cathartic/expressive or revealed too much information were reported at a higher rate for Twitter. Regretted messages on Twitter also reached broader audiences. In addition, we found that participants who posted on Twitter became aware of, and tried to repair, regret more slowly than those reporting in-person regrets. From this comparison of Twitter and in-person regrets, we provide preliminary ideas for tools to help Twitter users avoid and cope with regret.
Organizational information practices can result in a variety of privacy problems that can increase consumers' concerns for information privacy. To explore the link between individuals and organizations regarding privacy, we study how institutional privacy assurances such as privacy policies and industry self-regulation can contribute to reducing individual privacy concerns. Drawing on Communication Privacy Management (CPM) theory, we develop a research model suggesting that an individual's privacy concerns form through a cognitive process involving perceived privacy risk, privacy control, and his or her disposition to value privacy. Furthermore, individuals' perceptions of institutional privacy assurances -- namely, perceived effectiveness of privacy policies and perceived effectiveness of industry privacy self-regulation -- are posited to affect the riskcontrol assessment from information disclosure, thus, being an essential component of privacy concerns. We empirically tested the research model through a survey that was administered to 823 users of four different types of websites: 1) electronic commerce sites, 2) social networking sites, 3) financial sites, and 4) healthcare sites. The results provide support for the majority of the hypothesized relationships. The study reported here is novel to the extent that existing empirical research has not explored the link between individuals' privacy perceptions and institutional privacy assurances. We discuss implications for theory and practice and provide suggestions for future research.
Extremely high correlations between repeated judgments of visual appeal of homepages shown for 50 milliseconds have been interpreted as evidence for a mere exposure effect [Lindgaard et al. 2006]. Continuing that work, the present research had two objectives. First, it investigated the relationship between judgments differing in cognitive demands. Second, it began to identify specific visual attributes that appear to contribute to different judgments. Three experiments are reported. All used the stimuli and viewing time as before. Using a paradigm known to disrupt processing beyond the stimulus offset, Experiment 1 was designed to ensure that the previous findings could not be attributed to such continued processing. Adopting a within-subject design, Experiment 2 investigated the extent to which judgments differing in cognitive demands (visual appeal, perceived usability, trustworthiness) may be driven by the visual characteristics of a Web page. It also enabled analyses of visual attributes that contributed most to the different judgments. Experiment 3 replicated Experiment 2 but using a between-subject design to ensure that no practice effect could occur. The results suggest that all three types of judgments are largely driven by visual appeal, but that cognitively demanding judgments are processed in a qualitatively different manner than visual appeal, and that they rely on somewhat different visual attributes. A model accounting for the results is provided.
Information privacy has been called one of the most important ethical issues of the informa-tion age. Public opinion polls show rising levels of concern about privacy among Americans. Against this backdrop, research into issues associated with information privacy is increasing. Based on a number of preliminary studies, it has become apparent that organizational practices, individuals' perceptions of these practices, and societal responses are inextricably linked in many ways. Theories regarding these relationships are slowly emerging. Unfortunately, researchers attempting to examine such relationships through confirmatory empirical approaches may be impeded by the lack of validated instruments for measuring individuals' concerns about organizational information privacy practices. To enable future studies in the information privacy research stream, we developed and validated an instrument that identifies and measures the primary dimensions of individuals' concerns about organizational information privacy practices. The development process included examinations of privacy literature; experience surveys and focus groups; and the use of expert judges. The result was a parsimonious 15-item instrument with four sub-scales tapping into dimensions of individuals' concerns about organizational information privacy practices. The instrument was rigorously tested and validated across several heterogenous populations, providing a high degree of confidence in the scales' validity, reliability, and generalizability.
This paper presents a general statistical methodology for the analysis of multivariate categorical data arising from observer reliability studies. The procedure essentially involves the construction of functions of the observed proportions which are directed at the extent to which the observers agree among themselves and the construction of test statistics for hypotheses involving these functions. Tests for interobserver bias are presented in terms of first-order marginal homogeneity and measures of interobserver agreement are developed as generalized kappa-type statistics. These procedures are illustrated with a clinical diagnosis example from the epidemiological literature.
Differential privacy has disparate impact on model accuracy
- Eugene Bagdasaryan
- Omid Poursaeed
- Vitaly Shmatikov
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly
Shmatikov. Differential privacy has disparate impact on
model accuracy. Advances in Neural Information Processing
Systems, 32:15479-15488, 2019.
Privacy by design: The 7 foundational principles. Information and privacy commissioner of Ontario
- Ann Cavoukian
Ann Cavoukian et al. Privacy by design: The 7 foundational
principles. Information and privacy commissioner of Ontario,
Canada, 5:12, 2009.
Generalised differential privacy for text document processing
- Natasha Fernandes
- Mark Dras
- Annabelle Mciver
Natasha Fernandes, Mark Dras, and Annabelle McIver.
Generalised differential privacy for text document processing.
In International Conference on Principles of Security and
Trust, pages 123-148. Springer, Cham, 2019.