* indicates equal contributions. publications by categories in reversed chronological order. generated by jekyll-scholar.
2023
ConPro
Tracking, But Make It Offline: The Privacy Implications of Scanning QR Codes Found in the World
Rayaan Siddiqi*, Shubham Singh*, Lenore Zuck, and Chris Kanich
In 7th Workshop on Technology and Consumer Protection (ConPro ’23), Co-located with the 44th IEEE Symposium on Security and Privacy, May 25, 2023, San Francisco, CA, USA, May 2023
Open Problems in (Un)fairness of the Retail Food Safety Inspection Process
Tanya Berger-Wolf, Allison Howell, Chris Kanich, Ian A. Kash, Moniba Keymanesh, Barbara Kowalcyk, Gina Nicholson Kramer, Andrew Perrault, and Shubham Singh
In Responsible Decision Making in Dynamic Environments Workshop, Held in Conjunction with ICML 2022, July 23, 2022, Baltimore MD, USA, Jul 2022
With the ubiquity of data breaches, forgotten-about files stored in the cloud create latent privacy risks. We take a holistic approach to help users identify sensitive, unwanted files in cloud storage. We first conducted 17 qualitative interviews to characterize factors that make humans perceive a file as sensitive, useful, and worthy of either protection or deletion. Building on our findings, we conducted a primarily quantitative online study. We showed 108 long-term users of Google Drive or Dropbox a selection of files from their accounts. They labeled and explained these files’ sensitivity, usefulness, and desired management (whether they wanted to keep, delete, or protect them). For each file, we collected many metadata and content features, building a training dataset of 3,525 labeled files. We then built Aletheia, which predicts a file’s perceived sensitivity and usefulness, as well as its desired management. Aletheia improves over state-of-the-art baselines by 26% to 159%, predicting users’ desired file-management decisions with 79% accuracy. Notably, predicting subjective perceptions of usefulness and sensitivity led to a 10% absolute accuracy improvement in predicting desired file-management decisions. Aletheia’s performance validates a human-centric approach to feature selection when using inference techniques on subjective security-related tasks. It also improves upon the state of the art in minimizing the attack surface of cloud accounts.
2020
NetSci-X
NeXLink: Node Embedding Framework for Cross-Network Linkages Across Social Networks
Rishabh Kaushal, Shubham Singh, and Ponnurangam Kumaraguru
In Proceedings of NetSci-X 2020: Sixth International Winter School and Conference on Network Science, Aug 2020
Users create accounts on multiple social networks to get connected to their friends across these networks. We refer to these user accounts as user identities. Since users join multiple social networks, therefore, there will be cases where a pair of user identities across two different social networks belong to the same individual. We refer to such pairs as Cross-Network Linkages (CNLs). In this work, we model the social network as a graph to explore the question, whether we can obtain effective social network graph representation such that node embeddings of users belonging to CNLs are closer in embedding space than other nodes, using only the network information. To this end, we propose a modular and flexible node embedding framework, referred to as NeXLink, which comprises of three steps. First, we obtain local node embeddings by preserving the local structure of nodes within the same social network. Second, we learn the global node embeddings by preserving the global structure, which is present in the form of common friendship exhibited by nodes involved in CNLs across social networks. Third, we combine the local and global node embeddings, which preserve local and global structures to facilitate the detection of CNLs across social networks. We evaluate our proposed framework on an augmented (synthetically generated) dataset of 63,713 nodes & 817,090 edges and real-world dataset of 3338 Twitter-Foursquare node pairs. Our approach achieves an average Hit@1 rate of 98% for detecting CNLs across social networks and significantly outperforms previous state-of-the-art methods.
2019
ACM SAC
KidsGUARD: Fine Grained Approach for Child Unsafe Video Representation and Detection
Shubham Singh, Rishabh Kaushal, Arun Balaji Buduru, and Ponnurangam Kumaraguru
In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, Aug 2019
Increasingly more and more videos are being uploaded on video sharing platforms, and a significant number of viewers on these platforms are children. At times, these videos have violent or sexually explicit scenes (referred as child unsafe) to catch children’s attention. To evade moderation, malicious video uploaders typically limit the child unsafe content to only a few frames in the video. Hence, a fine-grained approach, referred as KidsGUARD1, to detect sparsely present child unsafe content is required. Prior approaches to content moderation either flag the entire video as inappropriate or use hand-crafted features derived from video frames. In this work, we leverage Long Short Term Memory (LSTM) based autoencoder to learn effective video representations of video descriptors obtained from using VGG16 Convolutional Neural Network (CNN). Encoded video representations are fed into LSTM classifier for detection of sparse child unsafe video content. To evaluate this approach, we create a dataset of 109,835 video clips curated specifically for child unsafe content. We find that deep learning approach (1) detects fine-grained child unsafe video content with the granularity of 1 second, (2) identifies even sparsely location child unsafe video content by achieving a high recall of 81% at high precision of 80%, and (3) outperforms baseline video encoding approaches based on like Fisher Vector (FV) and Vector of Locally Aggregated Descriptors (VLAD).