Analysing privacy in visual lifelogging

The visual lifelogging activity enables a user, the lifelogger, to passively capture images from a first-person perspective and ultimately create a visual diary encoding every possible aspect of her life with unprecedented details. In recent years, it has gained popularities among different groups of users. However, the possibility of ubiquitous presence of lifelogging devices specifically in private spheres has raised serious concerns with respect to personal privacy. In this article, we have presented a thorough discussion of privacy with respect to visual lifelogging. We have re-adjusted the existing definition of lifelogging to reflect different aspects of privacy and introduced a first-ever privacy threat model identifying several threats with respect to visual lifelogging. We have also shown how the existing privacy guidelines and approaches are inadequate to mitigate the identified threats. Finally, we have outlined a set of requirements and guidelines that can be used to mitigate the identified threats while designing and developing a privacy-preserving framework for visual lifelogging

Lifelogging user study:bystander privacy

Automatically and passively taking pictures (using lifelogging devices such as wearable cameras) of people who don’t know they’re having their picture taken raises a number of privacy concerns (from a bystander’s perspective). We conducted a study focussing on the bystanders’ concerns to the presence of augmented reality wearable devices in two contexts (one formal and one informal). The results suggests the need to embed privacy enhancing techniques into the design of lifelogging applications, which are likely to depend upon an array of factors, but not limited to the context of use, scenario (and surroundings), and content

Privacy threat model in lifelogging

The lifelogging activity enables a user, the lifelogger, to passively capture multimodal records from a first-person perspective and ultimately create a visual diary encompassing every possible aspect of her life with unprecedented details. In recent years it has gained popularity among different groups of users. However, the possibility of ubiquitous presence of lifelogging devices especially in private spheres has raised serious concerns with respect to personal privacy. Different practitioners and active researchers in the field of lifelogging have analysed the issue of privacy in lifelogging and proposed different mitigation strategies. However, none of the existing works has considered a well-defined privacy threat model in the domain of lifelogging. Without a proper threat model, any analysis and discussion of privacy threats in lifelogging remains incomplete. In this paper we aim to fill in this gap by introducing a first-ever privacy threat model identifying several threats with respect to lifelogging. We believe that the introduced threat model will be an essential tool and will act as the basis for any further research within this domain

A privacy by design approach to lifelogging

Technologies that enable us to capture and publish data with ease are likely to pose new concerns about privacy of the individual. In this article we exam- ine the privacy implications of lifelogging, a new concept being explored by early adopters, which utilises wearable devices to generate a media rich archive of their life experience. The concept of privacy and the privacy implications of lifelogging are presented and discussed in terms of the four key actors in the lifelogging uni- verse. An initial privacy-aware lifelogging framework, based on the key principles of privacy by design is presented and motivated

A user-study examining visualization of lifelogs

With continuous advances in the pervasive sensing and lifelogging technologies for the quantified self, users now can record their daily life activities automatically and seamlessly. In the existing lifelogging research, visualization techniques for presenting the lifelogs and evaluating the effectiveness of such techniques from a lifelogger's perspective has not been adequately studied. In this paper, we investigate the effectiveness of four distinct visualization techniques for exploring the lifelogs, which were collected by 22 lifeloggers who volunteered to use a wearable camera and a GPS device simultaneously, for a period of 3 days. Based on a user study with these 22 lifeloggers, which required them to browse through their personal lifelogs, we seek to identify the most effective visualization technique. Our results suggest various ways to augment and improve the visualization of personal lifelogs to enrich the quality of user experience and making lifelogging tools more engaging. We also propose a new visualization feature-drill-down approach with details-on-demand, to make the lifelogging visualization process more meaningful and informative to the lifeloggers

Exploring lifelog sharing and privacy

The emphasis on exhaustive passive capturing of images using wearable cameras like Autographer, which is often known as lifelogging has brought into foreground the challenge of preserving privacy, in addition to presenting the vast amount of images in a meaningful way. In this paper, we present a user-study to understand the importance of an array of factors that are likely to influence the lifeloggers to share their lifelog images in their online circle. The findings are a step forward in the emerging area intersecting HCI, and privacy, to help in exploring design directions for privacy mediating techniques in lifelogging applications

PrivacEye: Privacy-Preserving Head-Mounted Eye Tracking Using Egocentric Scene Image and Eye Movement Features

Eyewear devices, such as augmented reality displays, increasingly integrate eye tracking but the first-person camera required to map a user's gaze to the visual scene can pose a significant threat to user and bystander privacy. We present PrivacEye, a method to detect privacy-sensitive everyday situations and automatically enable and disable the eye tracker's first-person camera using a mechanical shutter. To close the shutter in privacy-sensitive situations, the method uses a deep representation of the first-person video combined with rich features that encode users' eye movements. To open the shutter without visual input, PrivacEye detects changes in users' eye movements alone to gauge changes in the "privacy level" of the current situation. We evaluate our method on a first-person video dataset recorded in daily life situations of 17 participants, annotated by themselves for privacy sensitivity, and show that our method is effective in preserving privacy in this challenging setting.Comment: 10 pages, 6 figures, supplementary materia

A User Study of a Wearable System to Enhance Bystanders’ Facial Privacy

The privacy of users and information are becoming increasingly important with the growth and pervasive use of mobile devices such as wearables, mobile phones, drones, and Internet of Things (IoT) devices. Today many of these mobile devices are equipped with cameras which enable users to take pictures and record videos anytime they need to do so. In many such cases, bystanders’ privacy is not a concern, and as a result, audio and video of bystanders are often captured without their consent. We present results from a user study in which 21 participants were asked to use a wearable system called FacePET developed to enhance bystanders’ facial privacy by providing a way for bystanders to protect their own privacy rather than relying on external systems for protection. While past works in the literature focused on privacy perceptions of bystanders when photographed in public/shared spaces, there has not been research with a focus on user perceptions of bystander-based wearable devices to enhance privacy. Thus, in this work, we focus on user perceptions of the FacePET device and/or similar wearables to enhance bystanders’ facial privacy. In our study, we found that 16 participants would use FacePET or similar devices to enhance their facial privacy, and 17 participants agreed that if smart glasses had features to conceal users’ identities, it would allow them to become more popular

Collecting Shared Experiences through Lifelogging: Lessons Learned

The emergence of widespread pervasive sensing, personal recording technologies, and systems for the quantified self are creating an environment in which one can capture fine-grained activity traces. Such traces have wide applicability in domains such as human memory augmentation, behavior change, and healthcare. However, obtaining these traces for research is nontrivial, especially those containing photographs of everyday activities. To source data for their own work, the authors created an experimental setup in which they collected detailed traces of a group of researchers over 2.75 days. They share their experiences of this process and present a series of lessons learned for other members of the research community conducting similar studies