Modern mobile operating systems implement an ask-on-first-use policy to regulate applications' access to private user data: the user is prompted to allow or deny access to a sensitive resource the first time an app attempts to use it. Prior research shows that this model may not adequately capture user privacy preferences because subsequent requests may occur under varying contexts. To address this shortcoming, we implemented a novel privacy management system in Android, in which we use contextual signals to build a classifier that predicts user privacy preferences under various scenarios. We performed a 37-person field study to evaluate this new permission model under normal device usage. From our exit interviews and collection of over 5 million data points from participants, we show that this new permission model reduces the error rate by 75% (i.e., fewer privacy violations), while preserving usability. We offer guidelines for how platforms can better support user privacy decision making.

Distributed meetings can be messy, particularly when the task requires collaboration around multimedia artifacts. Teams must not only share a variety of materials related to the work in real time, but also need to refer back to information after a meeting ends. While video tools make it relatively easy to have conversations at a distance, they are less adept at sharing and archiving multimedia content. We conducted a survey of and interviews with members of distributed teams to investigate how they create, use, and share multimedia content before, during, and after distributed meetings. Our findings shed light on decisions made and rationales used in selecting technologies to prepare for, conduct, and archive the results of a video-mediated distributed meeting. The results suggest a need for flexible interfaces for information sharing in multiple meeting contexts so content can be both easily referred to in the moment and also found again later.

In this paper we investigate effects of the content of interruptions and of the time of interruption delivery on mobile phones. We review related work and report on a naturalistic quasi-experiment using experience-sampling that showed that the receptivity to an interruption is influenced by its content rather than by its time of delivery in the employed modality of delivery - SMS. We also examined the underlying variables that increase the perceived quality of content and found that the factors interest, entertainment, relevance and actionability influence people's receptivity significantly. Our findings inform system design that seeks to provide context-sensitive information or to predict interruptibility and suggest the consideration of receptivity as an extension to the way we think and reason about interruptibility.

Existing technologies for file sharing differ widely in the granularity of control they give users over who can access their data; achieving finer-grained control generally requires more user effort. We want to understand what level of control users need over their data, by examining what sorts of access policies users actually create in practice.

As sharing personal media online becomes easier and widely spread, new privacy concerns emerge - especially when the persistent nature of the media and associated context reveals details about the physical and social context in which the media items were created. In a first-of-its-kind study, we use context-aware camerephone devices to examine privacy decisions in mobile and online photo sharing. Through data analysis on a corpus of privacy decisions and associated context data from a real-world system, we identify relationships between location of photo capture and photo privacy settings. Our data analysis leads to further questions which we investigate through a set of interviews with 15 users. The interviews reveal common themes in privacy considerations: security, social disclosure, identity and convenience. Finally, we highlight several implications and opportunities for design of media sharing applications, including using past privacy patterns to prevent oversights and errors.

Spyware is an increasing problem. Interestingly, many programs carrying spyware honestly disclose the activities of the software, but users install the software anyway. We report on a study of software installation to assess the effectiveness of different notices for helping people make better decisions on which software to install. Our study of 222 users showed that providing a short summary notice, in addition to the End User License Agreement (EULA), before the installation reduced the number of software installations significantly. We also found that providing the short summary notice after installation led to a significant number of uninstalls. However, even with the short notices, many users installed the program and later expressed regret for doing so. These results, along with a detailed analysis of installation, regret, and survey data about user behaviors informs our recommendations to policymakers and designers for assessing the "adequacy" of consent in the context of software that exhibits behaviors associated with spyware.

We describe an approach for extracting semantics of tags, unstructured text-labels assigned to resources on the Web, based on each tag's usage patterns. In particular, we focus on the problem of extracting place and event semantics for tags that are assigned to photos on Flickr, a popular photo sharing website that supports time and location (latitude/longitude) metadata. We analyze two methods inspired by well-known burst-analysis techniques and one novel method: Scale-structure Identification. We evaluate the methods on a subset of Flickr data, and show that our Scale-structure Identification method outperforms the existing techniques. The approach and methods described in this work can be used in other domains such as geo-annotated web pages, where text terms can be extracted and associated with usage patterns.

We address the problem of extracting semantics of tags -- short, unstructured text-labels assigned to resources on the Web -- based on each tag's metadata patterns. In particular, we describe an approach for extracting place and event semantics for tags that are assigned to photos on Flickr, a popular photo sharing website supporting time and location (latitude/longitude) metadata. The approach can be generalized to other domains where text terms can be extracted and associated with metadata patterns, such as geo-annotated web pages.

The goal of a recommender system is to suggest items of interest to a user based on historical behavior of a community of users. Given detailed enough history, item-based collaborative filtering (CF) often performs as well or better than almost any other recommendation method. However, in cold-start situations - where a user, an item, or the entire system is new - simple non-personalized recommendations often fare better. We improve the scalability and performance of a previous approach to handling cold-start situations that uses filterbots, or surrogate users that rate items based only on user or item attributes. We show that introducing a very small number of simple filterbots helps make CF algorithms more robust. In particular, adding just seven global filterbots improves both user-based and item-based CF in cold-start user, cold-start item, and cold-start system settings. Performance is better when data is scarce, performance is no worse when data is plentiful, and algorithm efficiency is negligibly affected. We systematically compare a non-personalized baseline, user-based CF, item-based CF, and our bot-augmented user- and item-based CF algorithms using three data sets (Yahoo! Movies, MovieLens, and EachMovie) with the normalized MAE metric in three types of cold-start situations. The advantage of our "naïve filterbot" approach is most pronounced for the Yahoo! data, the sparsest of the three data sets.

As cameraphones become the dominant platform for consumer multimedia capture worldwide, multimedia researchers are faced both with the challenge of how to help users manage the billions of photographs they are collectively producing and the opportunity to leverage cameraphones' ability to automatically capture temporal, spatial, and social contextual metadata to help manage consumer multimedia content. In our Mobile Media Metadata 2 (MMM2) prototype, we apply collaborative filtering techniques to automatically gathered contextual metadata to infer the likely sharing recipients for photos captured on cameraphones. We show that while current cameraphone sharing interfaces are fraught with difficulty, it is possible to use a context-aware approach to make the sharing of cameraphone photos simpler and more satisfying for users. Based on our analysis of the relative contributions of different cameraphone sensors to predicting the likely recipients for photos, we discover for our user population that the temporal context of photo capture proved highly predictive of photo sharing behavior.

In this paper, we focus on the use of context-aware, collaborative filtering, machine-learning techniques that leverage automatically sensed and inferred contextual metadata together with computer vision analysis of image content to make accurate predictions about the human subjects depicted in cameraphone photos. We apply Sparse-Factor Analysis (SFA) to both the contextual metadata gathered in the MMM2 system and the results of PCA (Principal Components Analysis) of the photo content to achieve a 60% face recognition accuracy of people depicted in our cameraphone photos, which is 40% better than media analysis alone. In short, we use context-aware media analysis to solve the face recognition problem for cameraphone photos.

Spyware is a significant problem for most computer users. The term "spyware" loosely describes a new class of computer software. This type of software may track user activities online and offline, provide targeted advertising and/or engage in other types of activities that users describe as invasive or undesirable.While the magnitude of the spyware problem is well documented, recent studies have had only limited success in explaining the broad range of user behaviors that contribute to the proliferation of spyware. As opposed to viruses and other malicious code, users themselves often have a choice whether they want to install these programs.In this paper, we discuss an ecological study of users installing five real world applications. In particular, we seek to understand the influence of the form and content of notices (e.g., EULAs) on user's installation decisions.Our study indicates that while notice is important, notice alone may not be enough to affect users' decisions to install an application. We found that users have limited understanding of EULA content and little desire to read lengthy notices. Users found short, concise notices more useful, and noticed them more often, yet they did not have a significant effect on installation for our population. When users were informed of the actual contents of the EULAs to which they agreed, we found that users often regret their installation decisions.We discovered that regardless of the bundled content, users will often install an application if they believe the utility is high enough. However, we discovered that privacy and security become important factors when choosing between two applications with similar functionality. Given two similar programs (e.g. KaZaA and Edonkey), consumers will choose the one they believe to be less invasive and more stable. We also found that providing vague information in EULAs and short notices can create an unwarranted impression of increased security. In these cases, it may be helpful to have a standardized format for assessing the possible options and trade-offs between applications.

This paper reports on the results of studies of task management to support the design of a task list manager. We examined the media used to record and organize to-dos and tracked how tasks are completed over time. Our work shows that, contrary to popular wisdom, people are not poor at prioritizing. Rather, they have well-honed strategies for tackling particular task management challenges. By illustrating what factors influence task completion and how representations function to support task management, we hope to provide a strong foundation for the design of a personal to-do list manager. We also present some preliminary efforts in this direction.

As human-computer interaction becomes more closely modeled on human-human interaction, new techniques and strategies for human-computer interaction are required. In response to the inevitable shortcomings of recognition technologies, researchers have studied mediation: interaction techniques by which users can resolve system ambiguity and error. In this paper we approach the human-computer dialogue from the other side, examining system-initiated direction and mediation of human action. We conducted contextual interviews with a variety of experts in fields involving human-human direction, including a film director, photographer, golf instructor, and 911 operator. Informed by these interviews and a review of prior work, we present strategies for directing physical human action and an associated design space for systems that perform such direction. We illustrate these concepts with excerpts from our interviews and with our implemented system for automated media capture or Active Capture, in which an unaided computer system uses techniques identified in our design space to act as a photographer, film director, and cinematographer.

The recent popularity of mobile camera phones allows for new opportunities to gather important metadata at the point of capture. This paper describes a method for generating metadata for photos using spatial, temporal, and social context. We describe a system we implemented for inferring location information for pictures taken with camera phones and its performance evaluation. We propose that leveraging contextual metadata at the point of capture can address the problems of the semantic and sensory gaps. In particular, combining and sharing spatial, temporal, and social contextual metadata from a given user and across users allows us to make inferences about media content.

P2P file sharing systems such as Gnutella, Freenet, and KaZaA, while primarily intended for sharing multimedia files, frequently allow other types of information to be shared. This raises serious concerns about the extent to which users may unknowingly be sharing private or personal information.In this paper, we report on a cognitive walkthrough and a laboratory user study of the KaZaA file sharing user interface. The majority of the users in our study were unable to tell what files they were sharing, and sometimes incorrectly assumed they were not sharing any files when in fact they were sharing all files on their hard drive. An analysis of the KaZaA network suggested that a large number of users appeared to be unwittingly sharing personal and private files, and that some users were indeed taking advantage of this and downloading files containing ostensibly private information.

Users working with documents that are too large and detailed to fit on the user's screen (e.g. chip designs) have the choice between zooming or applying appropriate visualization techniques. In this paper, we present a comparison of three such techniques. The first, focus plus context screens, are wall-size low-resolution displays with an embedded high-resolution display region. This technique is compared with overview plus detail and zooming/panning. We interviewed fourteen visual surveillance and design professionals from different areas (graphic design, chip design, air traffic control, etc.) in order to create a repre sentative sample of tasks to be used in two experimental comparison studies. In the first experiment, subjects using focus plus context screens to extract information from large static documents completed the two experimental tasks on average 21% and 36% faster than when they used the other interfaces. In the second experiment, focus plus context screens allowed subjects to reduce their error rate in a driving simulation to less than one third of the error rate of the competing overview plus detail setup

Computer users working with large visual documents, such as large layouts, blueprints, or maps perform tasks that require them to simultaneously access overview information while working on details. To avoid the need for zooming, users currently have to choose between using a sufficiently large screen or applying appropriate visualization techniques. Currently available hi-res "wall-size" screens, however, are cost-intensive, space-intensive, or both. Visualization techniques allow the user to more efficiently use the given screen space, but in exchange they either require the user to switch between multiple views or they introduce distortion.In this paper, we present a novel approach to simultaneously display focus and context information. Focus plus context screens consist of a hi-res display and a larger low-res display. Image content is displayed such that the scaling of the display content is preserved, while its resolution may vary according to which display region it is displayed in. Focus plus context screens are applicable to practically all tasks that currently use overviews or fisheye views, but unlike these visualization techniques, focus plus context screens provide a single, non-distorted view. We present a prototype that seamlessly integrates an LCD with a projection screen and demonstrate four applications that we have adapted so far.