Privacy-preserving mechanisms for location privacy in mobile crowdsensing: A survey
With the advancement in communication techniques and sensor technologies, mobile
crowdsensing (MCS)—one of the most successful applications of crowdsourcing—has …
crowdsensing (MCS)—one of the most successful applications of crowdsourcing—has …
A survey of differential privacy-based techniques and their applicability to location-based services
The widespread use of mobile devices such as smartphones, tablets, and smartwatches has
led users to constantly generate various location data during their daily activities …
led users to constantly generate various location data during their daily activities …
[HTML][HTML] A survey of privacy-preserving mechanisms for heterogeneous data types
Due to the pervasiveness of always connected devices, large amounts of heterogeneous
data are continuously being collected. Beyond the benefits that accrue for the users, there …
data are continuously being collected. Beyond the benefits that accrue for the users, there …
Deep learning-based privacy-preserving framework for synthetic trajectory generation
Synthetic data generation based on state-of-the-art deep learning methods has recently
emerged as a promising solution to replace the expensive and laborious collection of real …
emerged as a promising solution to replace the expensive and laborious collection of real …
OpBoost: A vertical federated tree boosting framework based on order-preserving desensitization
Vertical Federated Learning (FL) is a new paradigm that enables users with non-
overlapping attributes of the same data samples to jointly train a model without directly …
overlapping attributes of the same data samples to jointly train a model without directly …
Context aware local differential privacy
Local differential privacy (LDP) is a strong notion of privacy that often leads to a significant
drop in utility. The original definition of LDP assumes that all the elements in the data …
drop in utility. The original definition of LDP assumes that all the elements in the data …
Privacy preservation in location-based services: A novel metric and attack model
Recent years have seen rising needs for location-based services in our everyday life. Aside
from the many advantages provided by these services, they have caused serious concerns …
from the many advantages provided by these services, they have caused serious concerns …
Local differential privacy on metric spaces: optimizing the trade-off with utility
M Alvim, K Chatzikokolakis… - 2018 IEEE 31st …, 2018 - ieeexplore.ieee.org
Local differential privacy (LPD) is a distributed variant of differential privacy (DP) in which the
obfuscation of the sensitive information is done at the level of the individual records, and in …
obfuscation of the sensitive information is done at the level of the individual records, and in …
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 …
Is geo-indistinguishability what you are looking for?
Since its proposal in 2013, geo-indistinguishability has been consolidated as a formal notion
of location privacy, generating a rich body of literature building on this idea. A problem with …
of location privacy, generating a rich body of literature building on this idea. A problem with …