Utility and Privacy in Object Tracking from Video Stream using Kalman Filter

Privacy and Utility Aware Data Sharing for Space Situational Awareness from Ensemble and Unscented Kalman Filtering Perspective

In this paper, we present an optimization-based formulation for privacy-utility tradeoff in the Ensemble and Unscented Kalman filtering framework, with a focus on space situational awareness. Privacy and utility are defined in terms of a lower and an upper bound on the state estimation error covariance, respectively. Synthetic sensor noise is used to satisfy these bounds and is determined by solving an optimization problem. Given privacy and utility bounds, we present optimization problem formulations to determine a) the maximum noise for which utility is satisfied or the estimation errors are…

差分隐私卡尔曼滤波中的误差界与隐私校准指南

Error Bounds and Guidelines for Privacy Calibration in Differentially Private Kalman Filtering

本文研究了在差分隐私保护下卡尔曼滤波的状态轨迹误差界和隐私校准方法，通过建立误差和熵界，为保持滤波误差在预设范围内提供隐私校准指导。

NUM-Based Rate Allocation for Streaming Traffic via Sequential Convex Programming

In recent years, there has been an increasing demand for ubiquitous streaming like applications in data networks. In this paper, we concentrate on NUM-based rate allocation for streaming applications with the so-called S-curve utility functions. Due to non-concavity of such utility functions, the underlying NUM problem would be non-convex for which dual methods might become quite useless. To tackle the non-convex problem, using elementary techniques we make the utility of the network concave, however this results in reverse-convex constraints which make the problem non-convex. To deal with suc…

Kalman-based Stochastic Gradient Method with Stop Condition and Insensitivity to Conditioning

Modern proximal and stochastic gradient descent (SGD) methods are believed to efficiently minimize large composite objective functions, but such methods have two algorithmic challenges: (1) a lack of fast or justified stop conditions, and (2) sensitivity to the objective function's conditioning. In response to the first challenge, modern proximal and SGD methods guarantee convergence only after multiple epochs, but such a guarantee renders proximal and SGD methods infeasible when the number of component functions is very large or infinite. In response to the second challenge, second order SGD …

Utility Maximization with a Stochastic Clock and an Unbounded Random Endowment

We introduce a linear space of finitely additive measures to treat the problem of optimal expected utility from consumption under a stochastic clock and an unbounded random endowment process. In this way we establish existence and uniqueness for a large class of utility maximization problems including the classical ones of terminal wealth or consumption, as well as the problems depending on a random time-horizon or multiple consumption instances. As an example we treat explicitly the problem of maximizing the logarithmic utility of a consumption stream, where the local time of an Ornstein-Uhle…

幂效用下的加性习惯：估计、渐近性与均衡

Additive habits with power utility: Estimates, asymptotics and equilibrium

本文研究离散时间市场与随机禀赋下带加性习惯的幂效用最大化问题，针对不完全市场建立最优消费流的估计并分析消费倾向的渐近行为，在完全市场中证明异质性个体经济体的Arrow-Debreu均衡存在性，并在代表性主体均衡中显式计算零息债券和Lucas树权益的价格及其对习惯形成参数的依赖。