On the privacy-conscientious use of mobile phone data
The breadcrumbs we leave behind when using our mobile phones—who somebody calls,
for how long, and from where—contain unprecedented insights about us and our societies …
for how long, and from where—contain unprecedented insights about us and our societies …
Mobility data science: Perspectives and challenges
Mobility data captures the locations of moving objects such as humans, animals, and cars.
With the availability of Global Positioning System (GPS)–equipped mobile devices and other …
With the availability of Global Positioning System (GPS)–equipped mobile devices and other …
Logan: Membership inference attacks against generative models
Generative models estimate the underlying distribution of a dataset to generate realistic
samples according to that distribution. In this paper, we present the first membership …
samples according to that distribution. In this paper, we present the first membership …
Knock knock, who's there? Membership inference on aggregate location data
Aggregate location data is often used to support smart services and applications, eg,
generating live traffic maps or predicting visits to businesses. In this paper, we present the …
generating live traffic maps or predicting visits to businesses. In this paper, we present the …
Edge-assisted public key homomorphic encryption for preserving privacy in mobile crowdsensing
R Ganjavi, AR Sharafat - IEEE Transactions on Services …, 2022 - ieeexplore.ieee.org
Mobile crowdsensing (MCS) is becoming an increasingly important topic due to rapid
proliferation of mobile apps where participants' anonymity is a pivotal requirement with direct …
proliferation of mobile apps where participants' anonymity is a pivotal requirement with direct …
Task allocation under geo-indistinguishability via group-based noise addition
Locations are usually necessary for task allocation in spatial crowdsourcing, which may put
individual privacy in jeopardy without proper protection. Although existing studies have well …
individual privacy in jeopardy without proper protection. Although existing studies have well …
Privacy-preserving aggregate mobility data release: An information-theoretic deep reinforcement learning approach
It is crucial to protect users' location traces against inference attacks on aggregate mobility
data collected from multiple users in various real-world applications. Most of the existing …
data collected from multiple users in various real-world applications. Most of the existing …
Pool Inference Attacks on Local Differential Privacy: Quantifying the Privacy Guarantees of Apple's Count Mean Sketch in Practice
Behavioral data generated by users' devices, ranging from emoji use to pages visited, are
collected at scale to improve apps and services. These data, however, contain fine-grained …
collected at scale to improve apps and services. These data, however, contain fine-grained …
PriSTE: from location privacy to spatiotemporal event privacy
Location privacy-preserving mechanisms (LPPMs) have been extensively studied for
protecting a user's location at each time point or a sequence of locations with different …
protecting a user's location at each time point or a sequence of locations with different …
A linear reconstruction approach for attribute inference attacks against synthetic data
Recent advances in synthetic data generation (SDG) have been hailed as a solution to the
difficult problem of sharing sensitive data while protecting privacy. SDG aims to learn …
difficult problem of sharing sensitive data while protecting privacy. SDG aims to learn …