Privacy-preserving mechanisms for location privacy in mobile crowdsensing: A survey

JW Kim, K Edemacu, B Jang - Journal of Network and Computer …, 2022 - Elsevier
With the advancement in communication techniques and sensor technologies, mobile
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

JW Kim, K Edemacu, JS Kim, YD Chung, B Jang - Computers & Security, 2021 - Elsevier
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 …

[HTML][HTML] A survey of privacy-preserving mechanisms for heterogeneous data types

M Cunha, R Mendes, JP Vilela - Computer science review, 2021 - Elsevier
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 …

Deep learning-based privacy-preserving framework for synthetic trajectory generation

JW Kim, B Jang - Journal of Network and Computer Applications, 2022 - Elsevier
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 …

OpBoost: A vertical federated tree boosting framework based on order-preserving desensitization

X Li, Y Hu, W Liu, H Feng, L Peng, Y Hong… - arXiv preprint arXiv …, 2022 - arxiv.org
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 …

Context aware local differential privacy

J Acharya, K Bonawitz, P Kairouz… - International …, 2020 - proceedings.mlr.press
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 …

Privacy preservation in location-based services: A novel metric and attack model

S Shaham, M Ding, B Liu, S Dang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
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 …

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 …

Task allocation under geo-indistinguishability via group-based noise addition

P Zhang, X Cheng, S Su, N Wang - IEEE Transactions on Big …, 2022 - ieeexplore.ieee.org
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 …

Is geo-indistinguishability what you are looking for?

S Oya, C Troncoso, F Pérez-González - … of the 2017 on Workshop on …, 2017 - dl.acm.org
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 …