Related papers: Towards Information Privacy for the Internet of Th…
Critical role of Internet of Things (IoT) in various domains like smart city, healthcare, supply chain and transportation has made them the target of malicious attacks. Past works in this area focused on centralized Intrusion Detection…
AI-based sensing at wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications, particularly for vision and perception tasks such as in autonomous driving and environmental monitoring. AI…
Internet of Things (IoT) devices are becoming increasingly popular and are influencing many application domains such as healthcare and transportation. These devices are used for real-world applications such as sensor monitoring, real-time…
The balance between protecting users' privacy while providing cost-effective devices that are functional and usable is a key challenge in the burgeoning Internet of Things (IoT) industry. While in traditional desktop and mobile contexts the…
Recent work~\cite{Liu2016} has shown that dependencies between items in a dataset can lead to privacy leaks. We extend this concept to privacy-preserving transformations, considering a broader set of dependencies captured by correlation…
For intelligent home IoT services with sensors and machine learning, we need to upload IoT data to the cloud server which cannot share private data for training. A recent machine learning approach, called federated learning, keeps user data…
Membership Inference Attacks have emerged as a dominant method for empirically measuring privacy leakage from machine learning models. Here, privacy is measured by the {\em{advantage}} or gap between a score or a function computed on the…
Billions of devices in the Internet of Things (IoT) collect sensitive data about people, creating data privacy risks and breach vulnerabilities. Accordingly, data privacy preservation is vital for sustaining the proliferation of IoT…
In this work, we provide an industry research view for approaching the design, deployment, and operation of trustworthy Artificial Intelligence (AI) inference systems. Such systems provide customers with timely, informed, and customized…
Recent years have witnessed the tremendous success of diffusion models in data synthesis. However, when diffusion models are applied to sensitive data, they also give rise to severe privacy concerns. In this paper, we systematically present…
We present a distributed average consensus protocol that preserves the privacy of agents' inputs. Unlike the differential privacy mechanisms, the presented protocol does not affect the accuracy of the output. It is shown that the protocol…
More data is almost always beneficial for analysis and machine learning tasks. In many realistic situations however, an enterprise cannot share its data, either to keep a competitive advantage or to protect the privacy of the data sources,…
Cooperative spectrum sensing, despite its effectiveness in enabling dynamic spectrum access, suffers from location privacy threats, merely because secondary users (SUs)' sensing reports that need to be shared with a fusion center to make…
The Internet of Things comes along with new challenges for experimenting, testing, and operating decentralized socio-technical systems at large-scale. In such systems, autonomous agents interact locally with their users, and remotely with…
The Internet of Things (IoT) systems are designed and developed either as standalone applications from the ground-up or with the help of IoT middleware platforms. They are designed to support different kinds of scenarios, such as smart…
Consider multiple users and a fusion center. Each user possesses a sequence of bits and can communicate with the fusion center through a one-way public channel. The fusion center's task is to compute the sum of all the sequences under the…
Differential privacy is often studied in one of two models. In the central model, a single analyzer has the responsibility of performing a privacy-preserving computation on data. But in the local model, each data owner ensures their own…
We design a scalable algorithm to privately generate location heatmaps over decentralized data from millions of user devices. It aims to ensure differential privacy before data becomes visible to a service provider while maintaining high…
This paper presents a new privacy negotiation mechanism for an IoT environment that is both efficient and practical to cope with the IoT special need of seamlessness. This mechanism allows IoT users to express and enforce their personal…
Privacy-preserving state estimation for linear time-invariant dynamical systems with crowd sensors is considered. At any time step, the estimator has access to measurements from a randomly selected sensor from a pool of sensors with…