A Privacy-Preserving User-Centric Data-Sharing Scheme

Using raw sensitive data of end-users helps service providers manage their operations efficiently and provide high-quality services to end-users. Although access to sensitive information benefits both parties, it poses several challenges concerning end-user privacy. Most data-sharing schemes based on differential privacy allow control of the level of privacy, which is not straightforward for end-users and leads to unpredictable utility. To address this issue, a novel local differentially private data-sharing scheme is proposed featuring a bimodal probability distribution that allows determining the range of random variables from which the noise is drawn with high probability. Additionally, a local differentially private mechanism is introduced to regulate the amount of noise injected into the data to control data utility. These components are combined to make up a user-centric data-sharing scheme which provides the end-user with control over the utility of their data, with the level of privacy being calculated from individual utility preferences. The simulation results show that the proposed scheme allows keeping the utility within the boundaries defined by the end-user, while providing the maximum possible level of privacy. Furthermore, it allows injecting more noise into the data for the same error in utility compared to the Laplace mechanism

Evaluating the Four-Way Performance Trade-Off for Data Stream Classification in Edge Computing

Edge computing (EC) is a promising technology capable of bridging the gap between Cloud computing services and the demands of emerging technologies such as the Internet of Things (IoT). Most EC-based solutions, from wearable devices to smart cities architectures, benefit from Machine Learning (ML) methods to perform various tasks, such as classification. In these cases, ML solutions need to deal efficiently with a huge amount of data, while balancing predictive performance, memory and time costs, and energy consumption. The fact that these data usually come in the form of a continuous and evolving data stream makes the scenario even more challenging. Many algorithms have been proposed to cope with data stream classification, e.g., Very Fast Decision Tree (VFDT) and Strict VFDT (SVFDT). Recently, Online Local Boosting (OLBoost) has also been introduced to improve predictive performance without modifying the underlying structure of the decision tree produced by these algorithms. In this work, we compared the four-way relationship among time efficiency, energy consumption, predictive performance, and memory costs, tuning the hyperparameters of VFDT and the two versions of SVFDT with and without OLBoost. Experiments over 6 benchmark datasets using an EC device revealed that VFDT and SVFDT-I were the most energy-friendly algorithms, with SVFDT-I also significantly reducing memory consumption. OLBoost, as expected, improved the predictive performance, but caused a deterioration in memory and energy consumption