| Title | Learning Light-Weight Edge-Deployable Privacy Models |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Lim, Yeon-sup, Srivatsa, Mudhakar, Chakraborty, Supriyo, Taylor, Ian |
| Conference Name | 2018 IEEE International Conference on Big Data (Big Data) |
| Keywords | anonymization framework, anonymized data, Computational modeling, Data models, data privacy, data schema, data-driven applications, deployable models, Entropy, Internet, Internet-of-Things devices, Lattices, light-weight data anonymization, light-weight edge-deployable privacy models, Measurement, Metrics, model learning, nonPC devices, privacy, privacy models and measurement, pubcrawl, sequential anonymization approach |
| Abstract | Privacy becomes one of the important issues in data-driven applications. The advent of non-PC devices such as Internet-of-Things (IoT) devices for data-driven applications leads to needs for light-weight data anonymization. In this paper, we develop an anonymization framework that expedites model learning in parallel and generates deployable models for devices with low computing capability. We evaluate our framework with various settings such as different data schema and characteristics. Our results exhibit that our framework learns anonymization models up to 16 times faster than a sequential anonymization approach and that it preserves enough information in anonymized data for data-driven applications. |
| DOI | 10.1109/BigData.2018.8622410 |
| Citation Key | lim_learning_2018 |
Learning Light-Weight Edge-Deployable Privacy Models