The Privacy Lab is led by Prof. Apu Kapadia in the School of Informatics and Computing at Indiana University. Our goal is to advance research in online privacy, mobile security, and peer-to-peer systems. For an overview of our research, see our Research Projects and Publications pages. Visit the People page to see the faces behind our research.
A new generation of wearable devices (such as Google Glass and the Narrative Clip) will soon make ‘first-person’ cameras nearly ubiquitous, capturing vast amounts of imagery without deliberate human action. ‘Lifelogging’ devices and applications will record and share images from people’s daily lives with their social networks.
We introduce PlaceAvoider, a technique for owners of first-person cameras to ‘blacklist’ sensitive spaces (like bathrooms and bedrooms). PlaceAvoider recognizes images captured in these spaces and flags them for review before the images are made available to applications. PlaceAvoider performs novel image analysis using both fine-grained image features (like specific objects) and coarse-grained, scene-level features (like colors and textures) to classify where a photo was taken.
Indiana University hosted the interactive and thought-provoking PETools workshop, chaired by Prof. Apu Kapadia and held in conjunction with PETS 2013. The goal of this workshop was to discuss the design of privacy tools aimed at real-world deployments. This workshop brought together privacy practitioners and researchers with the aim to spark dialog and collaboration between these communities. We thank the authors and attendees for a successful workshop! Please check out the program (with links to the abstracts)!
Prof. Apu Kapadia’s award from the National Science Foundation (NSF) is titled CAREER: Sensible Privacy: Pragmatic Privacy Controls in an Era of Sensor-Enabled Computing. From the press release: Kapadia will receive $550,887 over the next five years to advance his work in security and privacy in pervasive and mobile computing. Kapadia’s grant will allow him to pursue development of reactive privacy mechanisms that he said could have a profound and positive societal impact by not only helping people control their privacy, but also potentially increasing their participation in sensor-enabled computing. ”People need only care about the subset of data and usage scenarios that have the potential to violate their privacy, and this reduces the amount of data to which they must regulate access,” he said. “And people make better decisions concerning such access when these decisions are made in a context where they know how their data is being used.”
We introduce PlaceRaider, a proof-of-concept mobile malware that exploits a smartphone’s camera and onboard sensors to reconstruct rich, 3D models of the victim’s indoor space using only opportunistically taken photos. Attackers can use these models to engage in remote reconnaissance and virtual theft of the victims’ environment. We substantiate this threat through human subject studies. Our paper was presented at the 20th Annual Network & Distributed System Security Symposium (NDSS) 2013. For more details, see the PlaceRaider project page.
Profs. David Crandall and Apu Kapadia have been awarded $50K of seed funding through the Faculty Research Support Program (FRSP) for their project titled Vision for Privacy: Privacy-aware Crowd Sensing using Opportunistic Imagery. A variety of powerful and potentially transformative `visual social sensing’ applications could be created by aggregating together data from cameras and sensors on smartphones and emerging technologies such as augmented reality glasses (e.g., by Google Project Glass). These applications, however, raise major privacy concerns because of the large amount of potentially private data that could be captured. This project investigates techniques to provide guarantees on privacy in the context of such applications. For more details, see our project page.
We propose Cachet, a peer-to-peer social-network architecture that provides strong security and privacy guarantees. We leverage cryptographic techniques to protect the confidentiality of data, and design a hybrid structured-unstructured overlay paradigm where social contacts act as trusted caches to help reduce the cryptographic as well as the communication overhead in the network. We presented our paper on Cachet at the 8th ACM International Conference on Emerging Networking Experiments and Technologies (CoNEXT) 2012. For more details, see the Cachet project page.