Memory-Saving Oblivious RAM for Trajectory Data via Hierarchical Generation of Dummy Access over Untrusted Cloud Environment

Taisho Sasada, Bernard Ousmane Sane, Bernard Ousmane Sane

2025

Abstract

The proliferation of smartphones and IoT devices has led to a rapid increase in the generation of trajectory data. Managing this continuously generated data poses a significant burden. To alleviate this burden, cloud databases have become widespread, leading to increased storage of data on servers managed by other individuals and organizations (third parties). However, if there are adversaries among these third parties, viewing the data contents could lead to personal information leaks and privacy violations. Therefore, there are expectations for the use of encrypted databases that allow searching and managing data while it remains encrypted (in ciphertext form), without revealing the contents. Since data owners (clients) encrypt their data before storing it, third parties cannot view the actual content. However, it is known that merely encrypting the data is not sufficient for security, as a vulnerability has been identified where the original data can be inferred from access patterns to the encrypted database even without seeing the actual data content. In this paper, we propose an anonymization method for access patterns on trajectory data in encrypted databases. For anonymization, we apply Oblivious Random Access Memory (ORAM), which generates dummy accesses alongside data aggregation and updates to make the original accesses unidentifiable. Trajectory data is often aggregated and updated on a trajectory basis rather than by individual points. Therefore, directly generating dummy accesses at the point level using ORAM leads to overhead in encrypted memory. In our proposed method, we separate the data storage memory into upper and lower levels to make access patterns unidentifiable at the trajectory level rather than the point level. The lower memory contains single points, while the upper memory contains multiple points (capable of representing part or all of a trajectory), and dummy accesses are generated using ORAM to make upper memory accesses mutually unidentifiable.

Paper Citation

in Harvard Style

Sasada T. and Sane B. (2025). Memory-Saving Oblivious RAM for Trajectory Data via Hierarchical Generation of Dummy Access over Untrusted Cloud Environment. In Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP; ISBN 978-989-758-735-1, SciTePress, pages 635-642. DOI: 10.5220/0013370100003899

in Bibtex Style

@conference{icissp25,
author={Taisho Sasada and Bernard Sane},
title={Memory-Saving Oblivious RAM for Trajectory Data via Hierarchical Generation of Dummy Access over Untrusted Cloud Environment},
booktitle={Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP},
year={2025},
pages={635-642},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013370100003899},
isbn={978-989-758-735-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP
TI - Memory-Saving Oblivious RAM for Trajectory Data via Hierarchical Generation of Dummy Access over Untrusted Cloud Environment
SN - 978-989-758-735-1
AU - Sasada T.
AU - Sane B.
PY - 2025
SP - 635
EP - 642
DO - 10.5220/0013370100003899
PB - SciTePress