Secure Data Sharing in Smart Homes: An Efficient Approach Based on Local Differential Privacy and Randomized Responses

Smart homes are smart spaces that contain devices connected to each other, collecting information and facilitating users’ comfortable living, safety, energy management features. To improve the quality of individuals’ life, device companies service providers data about user activities, needs, power consumption, etc.; these need be shared with privacy-preserving practices. In this paper, an effective approach securing transmission provider is based on local differential privacy (LDP), which enables residents provide statistics their usage as disturbances bloom filters. Randomized Aggregatable Privacy-Preserving Ordinal (RAPPOR) a technique allows sharing while pre-serving individual users. The proposed applies two randomized responses: permanent random response (PRR) instantaneous (IRR), then machine learning algorithms for decoding perturbation filters side. simulation results show achieves good performance in terms privacy-preserving, accuracy, recall, f-measure metrics. indicate that, LDP achieved utility when value ϵ = 0.95. classification accuracy between 95.4% 98% utilized techniques.