Data is used widely by service providers as input to inference systems to perform decision making for authorized tasks. The raw data however allows a service provider to infer other sensitive information it has not been authorized for. We formulate a data-driven inference privacy preserving framework that sanitizes data to prevent leakage of sensitive information present in the raw data while ensuring that the sanitized data is still compatible with the service provider's legacy inference system and provides maximal utility. We propose to use maximal correlation as a privacy metric and show its advantage over the variational method for approximating mutual information. We develop a practical implementation of maximal correlation and derive sufficient conditions under which the data-driven implementation converges to the true privacy metric in the large training sample size regime. Furthermore, we adopt maximum mean discrepancy and adversarial domain discriminator as techniques to regularize the domain of the sanitized data to ensure its legacy compatibility. Finally, we develop a deep learning model as an example of the proposed inference privacy framework. Numerical experiments verify the feasibility of our approach.

Real-time data transmissions from a vehicle enhance road safety and traffic efficiency by aggregating data in a central server for data analytics. When drivers share their instantaneous vehicular information for a service provider to perform a legitimate task, a curious service provider may also infer private information it has not been authorized for. In this paper, we propose a privacy preservation framework based on the Hilbert Schmidt Independence Criterion (HSIC) to sanitize driving data to protect the vehicle’s trajectory from adversarial inference while ensuring the data is still useful for driver behavior detection. We develop a deep learning model to learn the HSIC sanitizer and demonstrate through two datasets that our approach achieves better utility-privacy trade-offs when compared to three other benchmarks.

We propose a privacy preserving framework to sanitize data so as to eliminate private information while maximally retaining non-sensitive information. We regularize the domain of the sanitized data to make it compatible with a service provider's learning systems already in place for the raw data. Thus, our privacy preserving framework incurs no additional cost for the service provider. We present a probabilistic sanitizer to privatize the raw data and a variational method to approximate the mutual information between the sanitized data and raw data. We include maximum mean discrepancy and domain adaption as the domain regularization techniques, and average information leakage as the privacy metric. We present a deep learning model as an example of the proposed framework where the input data is an image. Numerical experiments verify the feasibility of our approach.

We consider a linear dynamical system in which the state vector consists of both public and private states. One or more sensors make measurements of the state vector and sends information to a fusion center, which performs the final state estimation. To achieve an optimal tradeoff between the utility of estimating the public states and protection of the private states, the measurements at each time step are linearly compressed into a lower dimensional space. Under the centralized setting where all measurements are collected by a single sensor, we propose an optimization problem and an algorithm to find the best compression matrix. Under the decentralized setting where measurements are made separately at multiple sensors, each sensor optimizes its own local compression matrix. We propose methods to separate the overall optimization problem into multiple sub-problems that can be solved locally at each sensor. We consider the cases where there is no message exchange between the sensors; and where each sensor takes turns to transmit messages to the other sensors. Simulations and empirical experiments demonstrate the efficiency of our proposed approach in allowing the fusion center to estimate the public states with good accuracy while preventing it from estimating the private states accurately.

We are concerned with a privacy-preserving problem in Kalman filter: a sensor releases a set of measurements to fusion center, who has perfect knowledge of the dynamical model, to allow it to estimate the public state, while prevent it from estimating the private state. We propose to linearly transform the original observation into a lower dimensional space before sending them to fusion center. Two privacy-utility tradeoffs are formulated: one concerns only at the current time step and the other concerns over two time steps. The transformation that leads to the optimal tradeoff can be found in closed-form. The privacy (estimation of private state) and utility (estimation of public state) are measured based on recursive Bayesian Cramér-Rao bound.

This paper considers the problem of decentralized, cooperative, and dynamic self-localization in wireless sensor networks. In particular, we are interested in a restrictive but very realistic scenario where few anchors are deployed and each anchor whose location is priori known may only communicate with very few agents (e.g. just one agent) whose location is unknown and to-be-estimated. The lack of agent-to-anchor communication links renders slow estimation convergence thereby demanding more message exchanges among the nodes i.e. agent-to-agent and agent-to-anchor. This urges us to propose an efficient localization method that needs less iterations (i.e. less message exchanges) to achieve a certain accuracy.