Analyse the performances of sequential private identifiers: a LorWan privacy orentied communication protocol. Efficient numerical method to compute the mass function and the CDF of streaks of repeating events. Expectation of packet loss computed using Markov chains for improved performance. This work is based on an extention of https://inria.hal.science/hal-04525080. Please cite this paper if you use this package in your research project.
Project description
LoRaWAN, a widely deployed LPWAN protocol, raises privacy concerns due to metadata exposure, particularly concerning the exploitation of stable device identifiers. For the first time in literature, we propose two privacy-preserving pseudonym schemes tailored for LoRaWAN: resolvable pseudonyms and sequential pseudonyms. We extensively evaluate their performance and applicability through theoretical analysis and simulations based on a large-scale real-world dataset of 71 million messages. We conclude that sequential pseudonyms are the best solution.
This repository analyses the performances of sequential private identifiers: a LoRaWAN privacy orentied communication protocol.
We use efficient numerical methods to compute the mass function and the CDF of streaks of repeating events. The expectation of packet loss is computed using Markov chains for improved performances.
Research work
This work is based on an extention of the follwoing research paper: https://inria.hal.science/hal-04525080 Please cite this paper if you use this package in your research project.
Installation
pip install spistats
Documentation
The full API documentation is available here https://jaalmoes.com/doc/spistats/.
Usage
Generating figures of the paper
python -m spistats.markov
Collision
import spistats as spi
col = spi.Collision(nbr_dev, nbr_adr, adr_per_dev)
Desynchronization
import spistats.desynchronization as dsync
packet_count = dsync.NumberOfPacketBeforeDsync(0.2,5)
packet_count = dsync.NumberOfPacketBeforeDsync_multi([0.2,0.3],5)
Plotting
import spistats.plot as plt
import spistats.desynchronization as dsync
dsync_count = dsync.NumberOfDsync(0.2,2,10)
plt.cdf(dsync_count)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file spistats-0.1.0.tar.gz
.
File metadata
- Download URL: spistats-0.1.0.tar.gz
- Upload date:
- Size: 1.1 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.0 CPython/3.12.3
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 64a61d063bf31212ef3d639d696cf3ab947d2d99c995c7ed4770086bcb8ddc8f |
|
MD5 | 5b55136411db2291dca8b98869389801 |
|
BLAKE2b-256 | 02a2194eb23ec4c0d90cd83616465505b53067c0b34c160c5a644e23752cee3b |
File details
Details for the file spistats-0.1.0-py3-none-any.whl
.
File metadata
- Download URL: spistats-0.1.0-py3-none-any.whl
- Upload date:
- Size: 21.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.0 CPython/3.12.3
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 47c016e22e0955c8522a438ae3b47eec229d471e0b19b5c36fc9f33ecd3049a1 |
|
MD5 | 3029247ea8d152015c77e39f02907867 |
|
BLAKE2b-256 | bbec28205c14bdd9215924a66bc784a95bf1dfba8d0e7bdaf5b631486a237a0a |