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We consider the problem of predicting cellular network performance (signal maps) from measurements collected by several mobile devices. We formulate the problem within the online federated learning framework: (i) federated learning (FL) enables users to collaboratively train a model, while keeping their training data on their devices; (ii) measurements are collected as users move around over time and are used for local training in an online fashion. We consider an honest-but-curious server, who observes the updates from target users participating in FL and infers their location using a deep leakage from gradients (DLG) type of attack, originally developed to reconstruct training data of DNN image classifiers. We make the key observation that a DLG attack, applied to our setting, infers the average location of a batch of local data, and can thus be used to reconstruct the target users' trajectory at a coarse granularity. We build on this observation to protect location privacy, in our setting, by revisiting and designing mechanisms within the federated learning framework including: tuning the FL parameters for averaging, curating local batches so as to mislead the DLG attacker, and aggregating across multiple users with different trajectories. We evaluate the performance of our algorithms through both analysis and simulation based on real-world mobile datasets, and we show that they achieve a good privacy-utility tradeoff.
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Tackling the Neighboring Network Hit Problem in Cellular Data
Most humans today have mobile phones. According to the GSMA, there are almost 10 billion mobile connections in the world every day. These devices automatically capture behavioral data from human society and store it in databases around the world. However, data capture has several challenges to deal with, especially if it comes from old sources. Obsolete technologies such as 2G and 3G represent two-thirds of the total devices. To the best of our knowledge, all previous work only eliminates obvious problems in the data or use well-curated data. Eliminating traces in a time series can lead to deviations and biases in further analyses, especially when we are studying small areas or groups of peoples in the city. In this work, we present two algorithms to solve the problem of the Neighboring Network Hit (NNH) and calculate the distributions of trips and traveled distances with greater precision in small areas or groups of peoples. The problem of NNH arises when a mobile device connects to cellular sites other than those defined in the network design, which complicates the analysis of space-time mobility. We use cellular device data from three cities in Chile, obtained from the mobile phone operator and duly anonymized. We compare our results with the Government's Origin and Destination Surveys and use a novel method to generate synthetic data to which errors are added in a controlled manner to evaluate the performance of our solution. We conclude that our algorithms improve results compared to naive methods, increasing the accuracy in the count of trips and, mainly, in the distance distributions.
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- Award ID(s):
- 1827505
- PAR ID:
- 10185776
- Date Published:
- Journal Name:
- 2019 IEEE International Conference on Big Data
- Page Range / eLocation ID:
- 2344 to 2353
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BigData47090.2019.9006005
