Roger Iyengar
Carnegie Mellon University
11 Papers
2 Citations
Roger Iyengar is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Computer science & Wearable computer. The author has an hindex of 4, co-authored 7 publications. Previous affiliations of Roger Iyengar include Washington University in St. Louis.
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Papers
Towards Practical Differentially Private Convex Optimization
Roger Iyengar,Joseph P. Near,Dawn Song,Om Thakkar,Abhradeep Thakurta,Lun Wang +5 more
- 01 May 2019
TL;DR: Approximate Minima Perturbation is presented, a novel algorithm that can leverage any off-the-shelf optimizer and can be employed without any hyperparameter tuning, thus making it an attractive technique for practical deployment.
Automated analysis of privacy requirements for mobile apps
Sebastian Zimmeck,Ziqi Wang,Lieyong Zou,Roger Iyengar,Bin Liu,Florian Schaub,Shomir Wilson,Norman Sadeh,Steven M. Bellovin,Joel R. Reidenberg +9 more
- 01 Jan 2017
TL;DR: This study introduces a scalable system to help analyze and predict Android apps’ compliance with privacy requirements, and shows the viability of combining machine learning-based privacy policy analysis with static code analysis of apps.
217
Finding a Choice in a Haystack: Automatic Extraction of Opt-Out Statements from Privacy Policy Text
Vinayshekhar Bannihatti Kumar,Roger Iyengar,Namita Nisal,Yuanyuan Feng,Hana Habib,Peter Story,Sushain Cherivirala,Margaret Hagan,Lorrie Faith Cranor,Shomir Wilson,Florian Schaub,Norman Sadeh +11 more
- 20 Apr 2020
TL;DR: The creation of two corpora of opt-out choices are described, which enable the training of classifiers to identify opt-outs in privacy policies and a web browser extension designed to present available opt- out choices to users as they browse the web is introduced.
Towards scalable edge-native applications
Junjue Wang,Ziqiang Feng,Shilpa George,Roger Iyengar,Padmanabhan Pillai,Mahadev Satyanarayanan +5 more
- 07 Nov 2019
TL;DR: This paper proposes an adaptation-based strategy to allow scaling up the number of concurrent edge-native applications on a resource-limited cloudlet and wireless network and demonstrates up to 40% reduction in offered load with minimal impact on latency on a variety of cognitive assistance tasks over non-adaptive approaches.
71
OpenRTiST: End-to-End Benchmarking for Edge Computing
Shilpa George,Thomas Eiszler,Roger Iyengar,Haithem Turki,Ziqiang Feng,Junjue Wang,Padmanabhan Pillai,Mahadev Satyanarayanan +7 more
TL;DR: OpenRTiST is an open-source application that is simultaneously compute-intensive, bandwidth-hungry, and latency-sensitive, and implements a form of augmented reality that lets you “see the world through the eyes of an artist.”