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

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

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

Learning and mining from personal digital archives

Given the explosion of new sensing technologies, data storage has become significantly cheaper and consequently, people increasingly rely on wearable devices to create personal digital archives. Lifelogging is the act of recording aspects of life in digital format for a variety of purposes such as aiding human memory, analysing human lifestyle and diet monitoring. In this dissertation we are concerned with Visual Lifelogging, a form of lifelogging based on the passive capture of photographs by a wearable camera. Cameras, such as Microsoft's SenseCam can record up to 4,000 images per day as well as logging data from several incorporated sensors. Considering the volume, complexity and heterogeneous nature of such data collections, it is a signifcant challenge to interpret and extract knowledge for the practical use of lifeloggers and others. In this dissertation, time series analysis methods have been used to identify and extract useful information from temporal lifelogging images data, without benefit of prior knowledge. We focus, in particular, on three fundamental topics: noise reduction, structure and characterization of the raw data; the detection of multi-scale patterns; and the mining of important, previously unknown repeated patterns in the time series of lifelog image data. Firstly, we show that Detrended Fluctuation Analysis (DFA) highlights the feature of very high correlation in lifelogging image collections. Secondly, we show that study of equal-time Cross-Correlation Matrix demonstrates atypical or non-stationary characteristics in these images. Next, noise reduction in the Cross-Correlation Matrix is addressed by Random Matrix Theory (RMT) before Wavelet multiscaling is used to characterize the `most important' or `unusual' events through analysis of the associated dynamics of the eigenspectrum. A motif discovery technique is explored for detection of recurring and recognizable episodes of an individual's image data. Finally, we apply these motif discovery techniques to two known lifelog data collections, All I Have Seen (AIHS) and NTCIR-12 Lifelog, in order to examine multivariate recurrent patterns of multiple-lifelogging users

Dredging Up the Past: Lifelogging, Memory and Surveillance

The term “lifelog” refers to a comprehensive archive of an individual\u27s quotidian existence, created with the help of pervasive computing technologies. Lifelog technologies would record and store everyday conversations, actions, and experiences of their users, enabling future replay and aiding remembrance. Products to assist lifelogging are already on the market; but the technology that will enable people fully and continuously to document their entire lives is still in the research and development phase. For generals, edgy artists and sentimental grandmothers alike, lifelogging could someday replace or complement, existing memory preservation practices. Like a traditional diary, journal or day-book, the lifelog could preserve subjectively noteworthy facts and impressions. Like an old-fashioned photo album, scrapbook or home video, it could retain images of childhood, loved-ones and travels. Like a cardboard box time capsule or filing cabinet it could store correspondence and documents. Like personal computing software, it could record communications data, keystrokes and internet trails. The lifelog could easily store data pertaining to purely biological states derived from continuous self-monitoring of, for example, heart rate, respiration, blood sugar, blood pressure and arousal. To the extent that it preserves personal experience for voluntary private consumption, electronic lifelogging looks innocent enough, as innocent as Blackberries, home movies, and snapshots in silver picture frames. But lifelogging could fuel excessive self-absorption, since users would be engaged in making multimedia presentations about themselves all the time. The availability of lifelogging technology might lead individuals to overvalue the otherwise transient details of their lives. Furthermore, the potential would be great for incivility, emotional blackmail, exploitation, prosecution and social control by government surrounding lifelog creation, content and accessibility. Existing privacy law and policy do not suggest meaningful limits on unwanted uses of lifelogging data. This parry of the costs and benefits commences a fuller discussion of lifelogging\u27s ethical and legal implications

A privacy-aware and secure system for human memory augmentation

The ubiquity of digital sensors embedded in today's mobile and wearable devices (e.g., smartphones, wearable cameras, wristbands) has made technology more intertwined with our life. Among many other things, this allows us to seamlessly log our daily experiences in increasing numbers and quality, a process known as ``lifelogging''. This practice produces a great amount of pictures and videos that can potentially improve human memory. Consider how a single photograph can bring back distant childhood memories, or how a song can help us reminisce about our last vacation. Such a vision of a ``memory augmentation system'' can offer considerable benefits, but it also raises new security and privacy challenges. Maybe obviously, a system that captures everywhere we go, and everything we say, see, and do, is greatly increasing the danger to our privacy. Any data breach of such a memory repository, whether accidental or malicious, could negatively impact both our professional and private reputation. In addition, the threat of memory manipulation might be the most worrisome aspect of a memory augmentation system: if an attacker is able to remove, add, or change our captured information, the resulting data may implant memories in our heads that never took place, or, in turn, accelerate the loss of other memories. Starting from such key challenges, this thesis investigates how to design secure memory augmentation systems. In the course of this research, we develop tools and prototypes that can be applied by researchers and system engineers to develop pervasive applications that help users capture and later recall episodic memories in a secure fashion. We build trusted sensors and protocols to securely capture and store experience data, and secure software for the secure and privacy-aware exchange of experience data with others. We explore the suitability of various access control models to put users in control of the plethora of data that the system captures on their behalf. We also explore the possibility of using in situ physical gestures to control different aspects regarding the capturing and sharing of experience data. Ultimately, this thesis contributes to the design and development of secure systems for memory augmentation