In the contemporary era of big data, recommendation systems play a crucial role in guiding our daily decision-making amidst an overwhelming array of choices. Personalized recommendations have become increasingly popular by tailoring suggestions to user profiles, preferences, and interests. While many existing systems rely on centralizing data for making recommendations, the revelation of sensitive information poses a significant privacy concern. Studies indicate the potential to de-identify anonymous users, exposing details such as political views or sexual orientations through seemingly innocuous data, like movie ratings. In this paper, we introduce a personalized privacy-preserving semi-centralized recommendation system in a social network known as trust-based social network (TSN) to address these privacy challenges. TSN addresses privacy concerns by semi-centralizing data, treating each node in the network as an independent social entities. Data are distributed to social entities within trusted social networks, and the recommendation service provider only collects obfuscated data from social entities through the adoption of a differential-privacy mechanism. Consequently, data within TSN are either protected within local trusted social networks or obfuscated outside of these networks. The final recommendation is generated by combining local suggestions from the trusted social network with obfuscated global suggestions from the service provider. The emphasis on local suggestions ensures highly personalized recommendations. Evaluation results demonstrate that TSN achieves high accuracy in recommendations while effectively safeguarding user privacy.

In the present era of big data, recommendation systems play a crucial role in our daily lives by assisting us in making quicker and more informed decisions from a vast array of choices. The concept of personalized recommendations has gained widespread popularity, offering suggestions based on user profiles, preferences, and/or interests. Although many existing systems centralize data for making recommendations, the revelation of sensitive data poses a privacy concern, as research indicates the potential to de-identify anonymous users. For instance, sensitive information such as political views or sexual orientations can be inferred from seemingly non-sensitive data like product review and ratings. In this paper, we present a privacy-preserving personalized recommendation system named P2RecSys to address these privacy issues. Our system takes a semi-decentralized approach by treating each node in the network as an agent. Data are distributed to each agent within trusted networks, and the recommendation service provider only collects obfuscated data from agents using a differential-privacy mechanism. Consequently, data in P2RecSys are either safeguarded within local trusted networks or obfuscated outside of these networks. The final recommendation is then generated by combining local suggestions from the trusted network with obfuscated global suggestions from the service provider. The emphasis on local suggestions allows for highly personalized recommendations. Evaluation results demonstrate that P2RecSys achieves high accuracy in recommendations while effectively safeguarding user privacy.

In the current big data era, recommendation systems play an important role in our daily life to help us make faster and better decisions from massive numbers of choices. Personalized recommendation has gained its popularity as it provides recommendations according to the user profile, preferences and/or interests. Many existing systems make recommendation by centralizing data. However, the exposure of sensitive data raises a privacy concern as research has shown that it is possible to de-identify anonymous users. Examples include inferring sensitive information (e.g., political views, sexual orientations) from non-sensitive data (e.g., movie ratings). In this paper, we present a personalized privacy-preserving recommendation system called Trust-based Agent Network (TAN). It tackles the privacy issue by semi-decentralizing data and treating each node in the network as an agent. As such, data are distributed to each agent within each trusted network, and the recommendation service provider collects only obfuscated data from agents by adopting the differential-privacy mechanism. Consequently, data in our TAN are either protected inside local trusted networks or obfuscated outside of trusted networks. Final recommendation can then be made by aggregating the local suggestions from the trusted network and obfuscated global suggestions from the service provider. Personalized recommendations can be made by putting more emphasize on local suggestions. Evaluation results show that our TAN leads to high accuracy and highly personalized recommendations while protecting privacy.

With advancements in technology, huge volumes of valuable data have been generated and collected at a rapid velocity from a wide variety of rich data sources. Examples of these valuable data include healthcare and disease data such as privacy-preserving statistics on patients who suffered from diseases like the coronavirus disease 2019 (COVID-19). Analyzing these data can be for social good. For instance, data analytics on the healthcare and disease data often leads to the discovery of useful information and knowledge about the disease. Explainable artificial intelligence (XAI) further enhances the interpretability of the discovered knowledge. Consequently, the explainable data analytics helps people to get a better understanding of the disease, which may inspire them to take part in preventing, detecting, controlling and combating the disease. In this paper, we present an explainable data analytics system for disease and healthcare informatics. Our system consists of two key components. The predictor component analyzes and mines historical disease and healthcare data for making predictions on future data. Although huge volumes of disease and healthcare data have been generated, volumes of available data may vary partially due to privacy concerns. So, the predictor makes predictions with different methods. It uses random forest With sufficient data and neural network-based few-shot learning (FSL) with limited data. The explainer component provides the general model reasoning and a meaningful explanation for specific predictions. As a database engineering application, we evaluate our system by applying it to real-life COVID-19 data. Evaluation results show the practicality of our system in explainable data analytics for disease and healthcare informatics.