Decision Tree Classification on Outsourced Data
This addresses privacy and efficiency challenges for clients with limited resources in outsourced data scenarios, though it is incremental as it builds on existing privacy models.
The paper tackles the problem of learning decision trees on outsourced private data by proposing a client-server method that uses anatomization/fragmentation for privacy, where the server handles most processing and clients have limited resources. Experiments show the method achieves accuracy close to non-private trees while significantly reducing client computing requirements.
This paper proposes a client-server decision tree learning method for outsourced private data. The privacy model is anatomization/fragmentation: the server sees data values, but the link between sensitive and identifying information is encrypted with a key known only to clients. Clients have limited processing and storage capability. Both sensitive and identifying information thus are stored on the server. The approach presented also retains most processing at the server, and client-side processing is amortized over predictions made by the clients. Experiments on various datasets show that the method produces decision trees approaching the accuracy of a non-private decision tree, while substantially reducing the client's computing resource requirements.