Privacy-preserving classifiers recognize shared mobility behaviours from WiFi network imperfect data
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Preprint |
| Publié: |
2018
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866929564506128384 |
|---|---|
| author | Manzanilla-Salazar, Orestes Sansò, Brunilde |
| author_facet | Manzanilla-Salazar, Orestes Sansò, Brunilde |
| contents | This paper proves the concept that it is feasible to accurately recognize specific human mobility shared patterns, based solely on the connection logs between portable devices and WiFi Access Points (APs), while preserving user's privacy. We gathered data from the Eduroam WiFi network of Polytechnique Montreal, making omission of device tracking or physical layer data. The behaviors we chose to detect were the movements associated to the end of an academic class, and the patterns related to the small break periods between classes. Stringent conditions were self-imposed in our experiments. The data is known to have errors noise, and be susceptible to information loss. No countermeasures were adopted to mitigate any of these issues. Data pre-processing consists of basic statistics that were used in aggregating the data in time intervals. We obtained accuracy values of 93.7 % and 83.3 % (via Bagged Trees) when recognizing behaviour patterns of breaks between classes and end-of-classes, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1807_06190 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | Privacy-preserving classifiers recognize shared mobility behaviours from WiFi network imperfect data Manzanilla-Salazar, Orestes Sansò, Brunilde Signal Processing Cryptography and Security Machine Learning Applications 62P30 I.5.4 This paper proves the concept that it is feasible to accurately recognize specific human mobility shared patterns, based solely on the connection logs between portable devices and WiFi Access Points (APs), while preserving user's privacy. We gathered data from the Eduroam WiFi network of Polytechnique Montreal, making omission of device tracking or physical layer data. The behaviors we chose to detect were the movements associated to the end of an academic class, and the patterns related to the small break periods between classes. Stringent conditions were self-imposed in our experiments. The data is known to have errors noise, and be susceptible to information loss. No countermeasures were adopted to mitigate any of these issues. Data pre-processing consists of basic statistics that were used in aggregating the data in time intervals. We obtained accuracy values of 93.7 % and 83.3 % (via Bagged Trees) when recognizing behaviour patterns of breaks between classes and end-of-classes, respectively. |
| title | Privacy-preserving classifiers recognize shared mobility behaviours from WiFi network imperfect data |
| topic | Signal Processing Cryptography and Security Machine Learning Applications 62P30 I.5.4 |
| url | https://arxiv.org/abs/1807.06190 |