Decision tree classification has become a prevailing technique for online diagnosis services. By outsourcing computation intensive tasks to a cloud server, cloud-assisted online diagnosis services are better ways for cases that the storage and computation requirements exceed the capability of medical institutions. With privacy concerns as well as intellectual property protection issues, the valuable diagnosis classifier and the sensitive user data should be protected against the cloud server. In this paper, we identify a work-flow for cloud-assisted online diagnosis services. We propose an efficient and secure decision tree classification scheme in the proposed work-flow. Specifically, the medical institution transforms a locally pre-trained decision tree classifier to a decision table, and later uses searchable symmetric encryption to encrypt the decision table. Then, the encrypted table is outsourced to the cloud server, and a user can submit encrypted physiological features to the cloud server and obtain an encrypted diagnosis prediction back. We provide formal security proofs to demonstrate that our scheme protects the confidentiality of the decision tree classifier and the user's data. The performance analysis shows that our scheme achieves faster-than-linear classification speed. Experimental evaluations show that our scheme requires several micro-seconds to process a diagnosis request in the tested datasets.

A mutual correlation between trajectories of two users is very helpful to real-life applications such as product recommendation and social media. While providing tremendous benefits, the releasing of correlated trajectories may leak sensitive social relations, due to potential links between mutual correlations and social relations. To the best of our knowledge, we take the first step to propose a mathematically rigorous n-body Laplace framework, satisfying "-differential privacy, which efficiently prevents a social relation inference through the mutual correlation between n-node trajectories of two users. The problem is mathematically formulated by defining a trajectory correlation score to measure the social relation between two users. Then, under the n-body Laplace framework, we propose two Lagrange Multiplier-based Differentially Private (LMDP) approaches to optimize the privacy budgets, for the data utility measured by location distances and the data utility measured by location correlations, i.e., UD-LMDP and UC-LMDP. Also, we present detailed analyses of privacy, data utility, adversary knowledge and the constrained optimizations. Finally, we perform experimental studies with real-life data. Our experimental results show that our proposed approaches achieve better privacy and data utility than the existing approaches.

New privacy implications are induced to individuals and families because of the time-series data classification problem in the Internet of Things such as appliance classifications in the smart grid. To prevent the adversary from inferring the household appliance classification used in the smart grid, Singular Spectrum Analysis (SSA) has been applied to the Local Differential Privacy (SSA-LDP). Firstly, Fourier spectrum noise has been added via the geometric sum, which has been proved to achieve Laplace noise distribution. Furthermore, we have proved that the sanitized data through the SSA-LDP is "-deferentially private for the adversary inference attack. In addition, to achieve better data utility, a formula has been obtained for the optimal Fourier spectrum noise by decomposing it into the superposition of power spectra of the dominant SSA eigenfilters. At last, experiments have been performed with a computer generated data set and a real-world smart-meter data set. Comparisons to other privacy approaches show that the optimized SSA-LDP does achieve better data utility for a given data privacy.

Existing indoor semantic recognition schemes are capable of discovering patterns through smartphone sensing; however, there is a lack of a device-free indoor semantic recognition system. In this paper, we propose WiFiMap+, which is a first-ever automatical inference system using WiFi signals to recognize highlevel indoor semantics from human activities and environments, where the high-level indoor semantics consist of indoor facilities and environments. To characterize the static indoor environments and dynamical human activities separately with channel state information (CSI), we propose a novel two-stream architecture to generate the spatial streams and the movement streams independently. Compared to the recent research on activity recognition, this two-stream architecture can make the content area of CSI samples extend from human activities to indoor environments. For obtaining accurate indoor environment detection, we propose a CSI-environment model with a spatial stream generation algorithm, which can reduce the effect of human activities on environment detection. For stable activity recognition, we also propose an environment-based testing sample representation method, which can utilize the environment knowledge to overcome the diversity of CSI caused by the environment changes. Finally, we implement WiFiMap+ using commercial WiFi devices and evaluate its performance for seven common semantic detection cases in sixroom scenarios. The experimental results show that our proposed WiFiMap+ is robust to the multi-room scenario and can achieve the average accuracy of 92.8% and the lowest accuracy of about 82%.