Related papers: On the Relationship Between Inference and Data Pri…

Differential privacy is a mathematical concept that provides an information-theoretic security guarantee. While differential privacy has emerged as a de facto standard for guaranteeing privacy in data sharing, the known mechanisms to…

Privacy is a fundamental requirement in distributed quantum sensing networks, where multiple clients estimate spatially distributed parameters using shared quantum resources while interacting with potentially untrusted servers. Despite its…

In recent years, differential privacy has been adopted by tech-companies and governmental agencies as the standard for measuring privacy in algorithms. In this article, we study differential privacy in Bayesian posterior sampling settings.…

The rise of connected personal devices together with privacy concerns call for machine learning algorithms capable of leveraging the data of a large number of agents to learn personalized models under strong privacy requirements. In this…

Internet of Things (IoT) is a novel paradigm, which not only facilitates a large number of devices to be ubiquitously connected over the Internet but also provides a mechanism to remotely control these devices. The IoT is pervasive and is…

The abundance of data collected by sensors in Internet of Things (IoT) devices, and the success of deep neural networks in uncovering hidden patterns in time series data have led to mounting privacy concerns. This is because private and…

There is a significant demand for indoor localization technology in smart buildings, and the most promising solution in this field is using RF sensors and fingerprinting-based methods that employ machine learning models trained on…

Recently, many innovations have been experienced in healthcare by rapidly growing Internet-of-Things (IoT) technology that provides significant developments and facilities in the health sector and improves daily human life. The IoT bridges…

Directly releasing those data raises privacy and liability (e.g., due to unauthorized distribution of such datasets) concerns since location data contain users' sensitive information, e.g., regular moving patterns and favorite spots. To…

Federated learning (FL) is an effective paradigm for distributed environments such as the Internet of Things (IoT), where data from diverse devices with varying functionalities remains localized while contributing to a shared global model.…

Consider updates arriving online in which the $t$th input is $(i_t,d_t)$, where $i_t$'s are thought of as IDs of users. Informally, a randomized function $f$ is {\em differentially private} with respect to the IDs if the probability…

The usage of technologically advanced devices has seen a boom in many domains, including education, automation, and healthcare; with most of the services requiring Internet connectivity. To secure a network, device identification plays key…

The Internet of Things (IoT) is an evolving network of billions of interconnected physical objects, such as numerous sensors, smartphones, wearables, and embedded devices. These physical objects, generally referred to as the smart objects,…

A common privacy issue in traditional machine learning is that data needs to be disclosed for the training procedures. In situations with highly sensitive data such as healthcare records, accessing this information is challenging and often…

Internet of Things (IoT) is one of the technological advancements of the twenty-first century which can improve living standards. However, it also imposes new types of security challenges, including device authentication, traffic types…

The literature on differential privacy almost invariably assumes that the data to be analyzed are fully observed. In most practical applications this is an unrealistic assumption. A popular strategy to address this problem is imputation, in…

The sensitivity metric in differential privacy, which is informally defined as the largest marginal change in output between neighboring databases, is of substantial significance in determining the accuracy of private data analyses.…

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…

With the growing advances in the Internet of Things (IoT) technology, IoT device management platforms are becoming increasingly important. We conducted a web-based survey and usability study with 43 participants who use IoT devices…

The rapid expansion of the Internet of Things (IoT) and its integration with backbone networks have heightened the risk of security breaches. Traditional centralized approaches to anomaly detection, which require transferring large volumes…