Sachin Kadam

Nitesh Yadav, Vashisht Kumar, Sachin Kadam

Bluetooth Low Energy (BLE) is widely used in modern IoT systems because it consumes very little power, saves energy, and allows for simple device connectivity; however, maintaining security and communication reliability remains a challenge. In this paper, an authentication system is designed using industry-grade BLE-enabled nodes (nRF5340 development kit) that include a peripheral node with a keypad for entering a PIN and a central node with an LCD display. The entered PIN is sent wirelessly from the peripheral node to the central node via BLE technology, where it is verified in real time and displayed as correct or incorrect. Next, only after successful authentication can the peripheral node send data to the central node. In addition to authentication, the peripheral node can measure temperature in real time using the temperature sensor interfaced to it and send it wirelessly to the central node, where it can be displayed on the LCD interface. Received Signal Strength Indicator (RSSI) values are collected during experiments under various scenarios to evaluate the system's performance. We see that the signal strength (measured in terms of RSSI values) is strong at close range but weak as distance increases, indicating a decaying logarithmic pattern. The system also has low latency, which allows for quick input and output, and it uses PIN-based authentication to ensure security and prevent misuse. The entire system seamlessly integrates communication, sensing, and security, making it suitable for smart access control and wireless monitoring systems, including home automation.

Sachin Kadam, Anna Scaglione, Nikhil Ravi et al.

The Differential Privacy (DP) literature often centers on meeting privacy constraints by introducing noise to the query, typically using a pre-specified parametric distribution model with one or two degrees of freedom. However, this emphasis tends to neglect the crucial considerations of response accuracy and utility, especially in the context of categorical or discrete numerical database queries, where the parameters defining the noise distribution are finite and could be chosen optimally. This paper addresses this gap by introducing a novel framework for designing an optimal noise Probability Mass Function (PMF) tailored to discrete and finite query sets. Our approach considers the modulo summation of random noise as the DP mechanism, aiming to present a tractable solution that not only satisfies privacy constraints but also minimizes query distortion. Unlike existing approaches focused solely on meeting privacy constraints, our framework seeks to optimize the noise distribution under an arbitrary $(ε, δ)$ constraint, thereby enhancing the accuracy and utility of the response. We demonstrate that the optimal PMF can be obtained through solving a Mixed-Integer Linear Program (MILP). Additionally, closed-form solutions for the optimal PMF are provided, minimizing the probability of error for two specific cases. Numerical experiments highlight the superior performance of our proposed optimal mechanisms compared to state-of-the-art methods. This paper contributes to the DP literature by presenting a clear and systematic approach to designing noise mechanisms that not only satisfy privacy requirements but also optimize query distortion. The framework introduced here opens avenues for improved privacy-preserving database queries, offering significant enhancements in response accuracy and utility.