Intelligent Pandemic Surveillance via Privacy-Preserving Crowdsensing

Abstract

Intelligently responding to a pandemic like Covid-19 requires sophisticated models over accurate real-time data, which is typically lacking at the start, e.g., due to deficient population testing. In such times, crowdsensing of spatially tagged disease-related symptoms provides an alternative way of acquiring real-time insights about the pandemic. Existing crowdsensing systems aggregate and release data for pre-fixed regions, e.g., counties. However, the insights obtained from such aggregates do not provide useful information about smaller regions - e.g., neighborhoods where outbreaks typically occur - and the aggregate-and-release method is vulnerable to privacy attacks. Therefore, we propose a novel differentially private method to obtain accurate insights from crowdsensed data for any number of regions specified by the users (e.g., researchers and a policy makers) without compromising privacy of the data contributors. Our approach, which has been implemented and deployed, informs the development of the future privacy-preserving intelligent systems for longitudinal and spatial data analytics.

Keywords: Covid-19; crowdsensing; differential privacy; disease surveillance; pandemic.

Figure 1.

The system has three components. The Crowdsensing component collects and stores user reports. Private quadtree builder dynamically partitions the space via a collection of DP quadtrees. Query computer uses the DP quadtrees to compute SR queries.

Figure 2.

(a) shows a partition of space created by a quadtree; black points represent the data; (b) gives the quadtree (max height = 3, count threshold = 1) for (a); (c) shows how to compute SR queries via the quadtree’s quadrants; the answer to the third query is 4 because a node only stores the count of its quadrant.

Figure 3.

This figure shows three methods of creating acceptable partitions of 8 days, i.e., d = 3. For (b)-(d) n = 3. Both rows and columns show the progression of time. The colored rectangles, together in each row, show the acceptable partition for the day labeling the row. Rectangles in each row give the groups in the acceptable partition, and each is uniquely identifiable by its color and pattern—therefore, any two rectangles with the same color and pattern across rows refer to the one unique group of days. (a) shows the naïve approach that groups all 8 days into one group (i.e., n = d = 8), and each is unique. (b) shows acceptable partitions for n = 3 and k = 3. The vertical connections explicitly show, as an example, the same unique group across different partitions. Similarly, (c) shows the acceptable partitions by the covering algorithm [14] where the groups are reordered in a particular way; compared to (a) and (b), this method produces a lesser number of unique groups. (d) explicitly shows reordering of the groups in terms of their sizes done via a circular-shift after every n = 3 days. (e) compares the three methods via boxplot of the noise (generated over 100 iterations) at the root level of a quadtree corresponding to the three methods given in (a), (b), and (c) for the same privacy risk.

Figure 4.

Private counts are DP answers to SR queries computed by our method from the actual Covid-19 case count. (a) depicts the stacked bar-chart of the case counts of the 5 NY counties with the most Covid-19 cases. For each day: (i) two stacked bars are given, the first for the actual counts, and the second for the private counts; and (ii) each bar gives the total Covid-19 cases for the past 14 days. (b) juxtaposes the heatmaps of the actual and private 14-day case counts (on a log scale) for NY state and Richmond County. (c) compares our method to a method based on a naïve spatial partitioning approach (i.e., DP data aggregation over partitions created by a fixed grid base partitioning with a cell-size of 1 Km²), which guarantees the same level of privacy. Both the methods are probabilistic, and therefore the boxplots are computed over 100 iterations.

Figure 5.

Private counts refer to the counts computed by our method from the actual Covid-19 case count. (a) juxtaposes the heatmaps of the cumulative counts at the state level, both actual and private, on 7/17/2020; (b) plots the relative error in cumulative counts using our method for the top 25 states (by case count) and the entire US over the period from 3/20/2020 – 9/1/2020. (c) plots Kendall’s τ (rank correlation coefficient [18]) of the two ranked lists of states obtained from the private and the actual answers of SR queries for counties; τ = 1 when the ranking is identical; (d) plots the 14 days moving average of both the private and the actual counts for New York, Texas, and the entire US over the same period as (b). Since our method is probabilistic, the private counts shown are the average over 100 iterations. (e) shows the box plot of the relative error of the moving average over these 100 iterations.