Social networking sites are starting to offer users services that provide information about their composition and behavior. LinkedIn’s ‘Who viewed my profile’ feature is an example. Providing information about content viewers to content publishers raises new privacy concerns for viewers of social networking sites. In the paper ‘Viewing the Viewers: Publishers’ Desires and Viewers’ Privacy Concerns in Social Networks we report on 718 Mechanical Turk respondents. 402 were surveyed on publishers’ use and expectations of information about their viewers, and 316 were surveyed about privacy behaviors and concerns in the face of such visibility. In some instances, such as dating sites, gender differences exist about what information respondents felt should be shared with publishers and required of viewers.
Various assistive devices are able to give greater independence to people with visual impairments both online and offline. Significant work remains to understand and address their safety, security, and privacy concerns, especially in the physical, offline world. People with visual impairments are particularly vulnerable to physical assault and theft, shoulder-surfing attacks, and being overheard during private conversations. In the paper ‘Understanding the Physical Safety, Security, and Privacy Concerns of People withe Visual Impairments’ we conduct two sets of interviews to find out how people with visual impairments manage these concerns and how assistive technologies can help. The paper also proposes design considerations for camera-based devices that would help people with visual impairments monitor for potential threats around them.
People with visual impairments face numerous obstacles in their daily lives. Due to these obstacles, people with visual impairments face a variety of physical privacy concerns. Researchers have recently studied how emerging technologies, such as wearable devices, can help these concerns. In the paper ‘Addressing Physical Safety, Security, and Privacy for People with Visual Impairments’ we conduct 19 interviews with participants who have visual impairments in the greater San Francisco metropolitan area. Our participants’ detailed accounts illuminated three topics related to physical privacy. The first is the safety and security concerns of people with visual impairments in urban environments, such as feared and actual instances of assault. The second being their behaviors and strategies for protecting physical safety. The last being refined design considerations for future wearable devices that could enhance their awareness of surrounding threats.
Social media gives the potential for people to freely communicate regardless of their status. In practice, social categories like gender may still bias online communication, replicating offline disparities. In the paper Twitter’s Glass Ceiling: The Effect of Perceived Gender on Online Visibility we study over 94,000 Twitter users to investigate the association between perceived gender and measures of online visibility. We find that users perceived as female experience a ‘glass ceiling’, similar to the barrier women face in attaining higher positions in companies. Being perceived as female is associated with more visibility for users in lower quartiles of visibility, but the opposite is true for the most visible users where being perceived male is strongly associated with more visibility. Our analysis suggest that gender presented in social media profiles likely frame interactions as well as perpetuates old inequalities online.
The IU Privacy Lab led by PI Apu Kapadia has four papers accepted at CHI 2015! The first paper titled Privacy Concerns and Behaviors of People with Visual Impairments is a qualitative study that reports on interviews with 14 visually impaired people and suggests new directions for improving the privacy of the visually impaired using wearable technologies. The second paper titled Crowdsourced Exploration of Security Configurations explores the use of crowdsourcing to efficiently determine restricted sets of permissions that can strike reasonable tradeoffs between privacy and usability for smartphone apps.
The third paper (Note) titled Sensitive Lifelogs: A Privacy Analysis of Photos from Wearable Cameras is a followup study to our UbiComp 2014 paper titled Privacy Behaviors of Lifeloggers using Wearable Cameras. For this Note we analyzed the photos collected in our lifelogging study, seeking to understand what makes a photo private and what we can learn about privacy in this new and very different context where photos are captured automatically by one’s wearable camera. The fourth paper (Note) titled Interrupt Now or Inform Later?: Comparing Immediate and Delayed Privacy Feedback follows up on our CHI 2014 paper titled Reflection or Action?: How Feedback and Control Affect Location Sharing Decisions. This Note explored the effect of providing immediate vs. delayed privacy feedback (e.g., for location accesses). We found that the sense of privacy violation was heightened when feedback was immediate, but not actionable, and has implications on how and when privacy feedback should be provided.
PIs Apu Kapadia and David Crandall at IU, and Denise Anthony at Dartmouth College, have received a $1.2M collaborative NSF award (IU Share: $800K) to study privacy in the context of wearable cameras over the next four years. The ubiquity of cameras, both traditional and wearable, will soon create a new era of visual sensing applications, raising significant implications for individuals and society, both beneficial and hazardous. This research couples a sociological understanding of privacy with an investigation of technical mechanisms to address these needs. Issues such as context (e.g., capturing images for public use may be okay at a public event, but not in the home) and content (are individuals recognizable?) will be explored both on technical and sociological fronts: What can we determine about images, what does this mean in terms of privacy risk, and how can systems protect against risk to privacy?
Read more about this grant, and our project. Here is a 90-second video!
Researchers have shown how ‘network alignment’ techniques can be used to map nodes from a reference graph into an anonymized social-network graph. These algorithms, however, are often sensitive to larger network sizes, the number of seeds, and noise~— which may be added to preserve privacy. We propose a divide-and-conquer approach to strengthen the power of such algorithms. Our approach partitions the networks into ‘communities’ and performs a two-stage mapping: first at the community level, and then for the entire network. Through extensive simulation on real-world social network datasets, we show how such community-aware network alignment improves de-anonymization performance under high levels of noise, large network sizes, and a low number of seeds. Read more in our paper, which will be presented at ACM CCS 2014.
PIs Kapadia and Crandall have received a 2014 Google Research Award for their research on privacy in the context of ‘lifelogging’ wearable cameras. We expect that these wearable cameras (see the Narrative Clip and the Autographer in addition to Google Glass) will become commonplace within the next few years, regularly capturing photos to record a first-person perspective of the wearer’s life. The goal of this project is to investigate and build automatic algorithms to organize images from lifelogging cameras, using a combination of computer vision and analysis of sensor data (like GPS, WiFi, accelerometers, etc.), thus empowering users to efficiently manage and share these images in a way that protects their privacy. As a first step, we proposed PlaceAvoider, an approach for recognizing (and avoiding) sensitive spaces within images. Read an article about this work by the MIT Technology Review. Read more about our project here.