The widespread integration of the Internet of Things (IoT) is crucial in advancing sustainable development. IoT service providers actively collect user data for analysis using sophisticated Deep Learning (DL) algorithms. This enables the extraction of valuable insights for business intelligence and improving service quality. However, as these datasets contain sensitive personal information, there is a risk of privacy breaches when DL models are employed. This vulnerability may result in Membership Inference Attacks (MIA), potentially leading to the unauthorized disclosure of highly sensitive data. Therefore, developing an efficient and privacy-preserving data analysis system for IoT is imperative. Recent research has highlighted the effectiveness of utilizing Bloom Filter (BF)-encoding in conjunction with Differential Privacy (DP) for safeguarding privacy during data analysis. Given its attributes of low complexity and high utility, this approach proves effective, particularly in resource-constrained IoT domains. With this in mind, we propose a novel framework for privacy-preserving IoT data analysis based on BF-encoded data. Our research introduces an innovative BF-encoding technique combined with Local Differential Privacy (LDP), capable of efficiently encoding various types of IoT data (such as facial images and smart-meter data) while maintaining privacy when integrated into DL algorithms for downstream analysis. Experimental results demonstrate that our BF-encoded data surpasses the utility of standard BF-encoded data when utilized in DL algorithms for downstream tasks, showcasing an approximate 30% improvement in classification accuracy. Furthermore, we assess the privacy of these DL models against MIA, revealing that attackers can only make random guesses with an accuracy of approximately 50%.

Network Function Virtualization (NFV) is gaining popularity among network operators to provide cost effective and dynamic network services. NFV enables faster service by deploying virtual instances of network functions. While serving dynamic and varying traffic demands, network operators can get benefit from knowing the requirement for the number of Virtual Network Functions (VNFs), ahead of time. VNF requirement prediction method mostly depends on the fluctuation of network traffic load. Predicting the required number of VNFs helps the operator to manage network resources in better ways. VNF prediction method is being considered as an interesting research field for researchers. In our research, we propose VNF requirement prediction methods based on Deep Neural Networks (DNN) and Long Short Term Memory (LSTM) Networks. We provide experimental results which show promising accuracy and improvement of our method, compared to machine learning approaches used before for VNF requirement prediction. Network resource management techniques can be benefited enormously from our higher accuracy based approaches.