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  1. Free, publicly-accessible full text available November 1, 2022
  2. We introduce a new algorithm, Construction of dIfferentially Private Empirical Distributions from a low-order marginal set tHrough solving linear Equations with 𝑙2 Regularization (CIPHER), that produces differentially private empirical joint distributions from a set of low-order marginals. CIPHER is conceptually simple and requires no more than decomposing joint probabilities via basic probability rules to construct a linear equation set and subsequently solve the equations. Compared to the full-dimensional histogram (FDH) sanitization, CIPHER has drastically lower requirements on computational storage and memory, which is practically attractive especially considering that the high-order signals preserved by the FDH sanitization are likely just samplemore »randomness and rarely of interest. Our experiments demonstrate that CIPHER outperforms the multiplicative weighting exponential mechanism in preserving original information and has similar or superior cost-normalized utility to FDH sanitization at the same privacy budget.« less
    Free, publicly-accessible full text available July 6, 2022
  3. Free, publicly-accessible full text available July 7, 2022
  4. The rapid growth of GPS technology and mobile devices has led to a massive accumulation of location data, bringing considerable benefits to individuals and society. One of the major usages of such data is travel time prediction, a typical service provided by GPS navigation devices and apps. Meanwhile, the constant collection and analysis of the individual location data also pose unprecedented privacy threats. We leverage the notion of geo-indistinguishability, an extension of differential privacy to the location privacy setting, and propose a procedure for privacy-preserving travel time prediction without collecting actual individual GPS trace data. We propose new concepts tomore »examine the impact of the geo-indistinguishability sanitization on the usefulness of GPS traces and provide analytical and experimental utility analysis for privacy-preserving travel time prediction. We also propose new metrics to measure the adversary error in learning individual GPS traces from the collected sanitized data. Our experiment results suggest that the proposed procedure provides travel time analysis with satisfactory accuracy at reasonably small privacy costs.« less
  5. Differential Privacy (DP) formalizes privacy in mathematical terms and provides a robust concept for privacy protection. DIfferentially Private Data Synthesis (DIPS) techniques produce and release synthetic individual-level data in the DP framework. One key challenge to develop DIPS methods is the preservation of the statistical utility of synthetic data, especially in high-dimensional settings. We propose a new DIPS approach, STatistical Election to Partition Sequentially (STEPS) that partitions data by attributes according to their importance ranks according to either a practical or statistical importance measure. STEPS aims to achieve better original information preservation for the attributes with higher importance ranks andmore »produce thus more useful synthetic data overall. We present an algorithm to implement the STEPS procedure and employ the privacy budget composability to ensure the overall privacy cost is controlled at the pre-specified value. We apply the STEPS procedure to both simulated data and the 2000–2012 Current Population Survey youth voter data. The results suggest STEPS can better preserve the population-level information and the original information for some analyses compared to PrivBayes, a modified Uniform histogram approach, and the flat Laplace sanitizer.« less
  6. A large amount of data is often needed to train machine learning algorithms with confidence. One way to achieve the necessary data volume is to share and combine data from multiple parties. On the other hand, how to protect sensitive personal information during data sharing is always a challenge. We focus on data sharing when parties have overlapping attributes but non-overlapping individuals. One approach to achieve privacy protection is through sharing differentially private synthetic data. Each party generates synthetic data at its own preferred privacy budget, which is then released and horizontally merged across the parties. The total privacy costmore »for this approach is capped at the maximum individual budget employed by a party. We derive the mean squared error bounds for the parameter estimation in common regression analysis based on the merged sanitized data across parties. We identify through theoretical analysis the conditions under which the utility of sharing and merging sanitized data outweighs the perturbation introduced for satisfying differential privacy and surpasses that based on individual party data. The experiments suggest that sanitized HOMM data obtained at a practically reasonable small privacy cost can lead to smaller prediction and estimation errors than individual parties, demonstrating the benefits of data sharing while protecting privacy.« less
  7. Mobile microrobots that maneuver in liquid environments and navigate inside the human body have drawn a great interest due to their possibility for medical uses serving as an in vivo cargo. For this system, the effective self-propelling method, which should be powered wirelessly and controllable in 3-D space, is of paramount importance. This article describes a bubble-powered swimming microdrone that can navigate in 3-D space in a controlled manner. To enable 3-D propulsion with steering capability, air bubbles of three lengths are trapped in microtubes that are embedded and three-dimensionally aligned inside the drone body using two-photon polymerization. These bubblesmore »can generate on-demand 3-D propulsion through microstreaming when they are selectively excited at their individual resonance frequencies that depend on the bubble sizes. In order to equip the drone with highly stable maneuverability, a non-uniform mass distribution of the drone body is carefully designed to spontaneously restore the drone to the upright position from disturbances. A mathematical model of the restoration mechanism is developed to predict the restoration behavior showing a good agreement with the experimental data. The present swimming microdrone potentially lends itself to a robust 3-D maneuverable microscale mobile cargo navigating in vitro and in vivo for biomedical applications.« less