Related papers: YouSense: Mitigating Entropy Selfishness in Distri…

In cognitive radio, spectrum sensing is a key component to detect spectrum holes (i.e., channels not used by any primary users). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking…

Sensitive data release is vulnerable to output-side privacy threats such as membership inference, attribute inference, and record linkage. This creates a practical need for release mechanisms that provide formal privacy guarantees while…

This paper studies cooperative spectrum sensing in cognitive radio networks where secondary users collect local energy statistics and report their findings to a secondary base station, i.e., a fusion center. First, the average error…

Eigenvalue-based detectors are considered as an important method of spectrum sensing since they do not require the information about the primary user (PU) signal. In this paper we propose a method to improve the performance of the…

Mobile sensor data has been proposed for security-critical applications such as device pairing, proximity detection, and continuous authentication. However, the foundational premise that these signals provide sufficient entropy remains…

We consider the problem of Spectrum Sensing in Cognitive Radio Systems. We have developed a distributed algorithm that the Secondary users can run to sense the channel cooperatively. It is based on sequential detection algorithms which…

In this work, we are concerned with maximizing the lifetime of a cluster of sensors engaged in single-hop communication with a base-station. In a data-gathering network, the spatio-temporal correlation in sensor data induces…

Underutilized wireless channel is a waste of spectral resource. Eavesdropping compromises data secrecy. How to overcome the two problems with one solution? In this paper, we propose a spectrum sharing model that defends against…

In this paper, ENTROPY platform, an IT ecosystem for supporting energy efficiency in buildings through behavioural change of the occupants is provided. The ENTROPY platform targets at providing a set of mechanisms for accelerating the…

Estimating the entropy of a discrete random variable is a fundamental problem in information theory and related fields. This problem has many applications in various domains, including machine learning, statistics and data compression. Over…

Compressive sampling has great potential for making wideband spectrum sensing possible at sub-Nyquist sampling rates. As a result, there have recently been research efforts that leverage compressive sampling to enable efficient wideband…

In this paper, we propose DSSP, a Distributed, SLO-aware, Sensing-domain-privacy-Preserving architecture for Sensing-as-a-Service (SaS). DSSP addresses four major limitations of the current SaS architecture. First, to improve sensing…

Human agents are increasingly serving as data sources in the context of dynamical systems. Unlike traditional sensors, humans may manipulate or omit data for selfish reasons. Therefore, this paper studies the influence of effort-averse…

Federated Learning allows distributed entities to train a common model collaboratively without sharing their own data. Although it prevents data collection and aggregation by exchanging only parameter updates, it remains vulnerable to…

How can we determine whether an AI system preserves itself as a deeply held objective or merely as an instrumental strategy? Autonomous agents with memory, persistent context, and multi-step planning create a measurement problem: terminal…

The security of any cryptosystem relies on the secrecy of the system's secret keys. Yet, recent experimental work demonstrates that tens of thousands of devices on the Internet use RSA and DSA secrets drawn from a small pool of candidate…

In this paper, we study the problem of privacy-preserving data sharing, wherein only a subset of the records in a database are sensitive, possibly based on predefined privacy policies. Existing solutions, viz, differential privacy (DP), are…

As sensors become ever more prevalent, more and more information will be collected about each of us. A longterm research question is how best to support beneficial uses while preserving individual privacy. Presence systems are an emerging…

This paper considers a simple on-off random multiple access channel, where n users communicate simultaneously to a single receiver over m degrees of freedom. Each user transmits with probability lambda, where typically lambda n < m << n,…

Distributed compressive sensing is a framework considering jointly sparsity within signal ensembles along with multiple measurement vectors (MMVs). The current theoretical bound of performance for MMVs, however, is derived to be the same…