Search Results for author:
Found 16 papers, 6 papers with code
AirGapAgent: Protecting Privacy-Conscious Conversational Agents
The growing use of large language model (LLM)-based conversational agents to manage sensitive user data raises significant privacy concerns.
Confidential Federated Computations
Federated Learning and Analytics (FLA) have seen widespread adoption by technology platforms for processing sensitive on-device data.
ViFiT: Reconstructing Vision Trajectories from IMU and Wi-Fi Fine Time Measurements
Tracking subjects in videos is one of the most widely used functions in camera-based IoT applications such as security surveillance, smart city traffic safety enhancement, vehicle to pedestrian communication and so on.
ViFi-Loc: Multi-modal Pedestrian Localization using GAN with Camera-Phone Correspondences
During inference, it generates refined position estimations based only on pedestrians' phone data that consists of GPS, IMU and FTM.
ViFiCon: Vision and Wireless Association Via Self-Supervised Contrastive Learning
We introduce ViFiCon, a self-supervised contrastive learning scheme which uses synchronized information across vision and wireless modalities to perform cross-modal association.
ViTag: Online WiFi Fine Time Measurements Aided Vision-Motion Identity Association in Multi-person Environments
ViTag associates a sequence of vision tracker generated bounding boxes with Inertial Measurement Unit (IMU) data and Wi-Fi Fine Time Measurements (FTM) from smartphones.
Vi-Fi: Associating Moving Subjects across Vision and Wireless Sensors
In this paper, we present Vi-Fi, a multi-modal system that leverages a user’s smartphone WiFi Fine Timing Measurements (FTM) and inertial measurement unit (IMU) sensor data to associate the user detected on a camera footage with their corresponding smartphone identifier (e. g. WiFi MAC address).
Algorithms for bounding contribution for histogram estimation under user-level privacy
In this scenario, the amount of noise injected into the histogram to obtain differential privacy is proportional to the maximum user contribution, which can be amplified by few outliers.
Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation
We design a scalable algorithm to privately generate location heatmaps over decentralized data from millions of user devices.
LaNet: Real-time Lane Identification by Learning Road SurfaceCharacteristics from Accelerometer Data
and yields 100% lane classification accuracy with 200 meters of driving data, achieving over 90% with just 100 m (correspondingly to roughly one minute of driving).
Advances and Open Problems in Federated Learning
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
Federated Learning with Autotuned Communication-Efficient Secure Aggregation
Federated Learning enables mobile devices to collaboratively learn a shared inference model while keeping all the training data on a user's device, decoupling the ability to do machine learning from the need to store the data in the cloud.
Sub-6GHz Assisted MAC for Millimeter Wave Vehicular Communications
This paper contributes to this active research area by proposing a sub-6GHz assisted mmWave MAC that decouples the mmWave data and control planes.
Networking and Internet Architecture
Recognizing Textures with Mobile Cameras for Pedestrian Safety Applications
Second, we aim at identifying when a distracted user is about to enter the street, which can be used to support safety functions such as warning the user to be cautious.
Reading Between the Pixels: Photographic Steganography for Camera Display Messaging
We sample from the resulting metamer sets to find color steps for each base color to embed a binary message into an arbitrary image with reduced visible artifacts.
Optimal Radiometric Calibration for Camera-Display Communication
We present a novel method for communicating between a camera and display by embedding and recovering hidden and dynamic information within a displayed image.
