With the rapid development of unmanned aerial vehicles (UAVs), often referred to as drones, their security issues are attracting more and more attention. Due to open-access communication environments, UAVs may raise security concerns, including authentication threats as well as the leakage of location and other sensitive data to unauthorized entities. Elliptic curve cryptography (ECC) is widely favored in authentication protocol design due to its security and performance. However, we found it still has the following two problems: inflexibility and a lack of backward security. This paper proposes an ECC-based identity authentication protocol LAPEC for UAVs. LAPEC can guarantee the backward secrecy of session keys and is more flexible to use. The time cost of LAPEC was analyzed, and its overhead did not increase too much when compared with other authentication methods.

The proliferation of high-performance personal devices and the widespread deployment of machine learning (ML) applications have led to two consequences: the volume of private data from individuals or groups has exploded over the past few years; and the traditional central servers for training ML models have experienced communication and performance bottlenecks in the face of massive amounts of data. However, this reality also provides the possibility of keeping data local for ML training and fusing models on a broader scale. As a new branch of ML application, Federated Learning (FL) aims to solve the problem of multi-party joint learning on the premise of protecting personal data privacy. However, due to the heterogeneity of devices, including network connection, network bandwidth, computing resources, etc., it is unrealistic to train, update and aggregate models in all devices in parallel, while personal data is often not independent and identically distributed (Non-IID) due to multiple reasons. This reality poses a challenge to the speed and convergence of FL. In this paper, we propose the pFedCAM algorithm, which aims to improve the robustness of the FL system to device heterogeneity and Non-IID data, while achieving some degree of federation model personalization. pFedCAM is based on the idea of clustering and model interpolation by classifying heterogeneous clients and performing FedAvg algorithm in parallel, and then combining them into personalized federated global models by inter-cluster model interpolation. Experiments show that the accuracy of pFedCAM improves 10.3% on Fashion-MNIST and 11.3% on CIFAR-10 compared to the benchmark in the case of Non-IID data. In the end, we applied pFedCAM in HomeProtect, a smart home privacy protection framework we designed, and achieved good practical results in the case of flame recognition.

With the widespread popularity of smart home devices and the emergence of smart home integration platforms such as Google, Amazon, and Xiaomi, the smart home industry is in a stage of vigorous development. While smart homes provide users with convenient and intelligent living, the problem of smart home devices leaking user privacy has become increasingly prominent. Smart home devices give users the ability to remotely control home devices, but they also reflect user home activities in traffic data, which brings the risk of privacy leaks. Potential attackers can use traffic classification technology to analyze traffic characteristics during traffic transmission (e.g., at the traffic exit of a smart home gateway) and infer users’ private information, such as their home activities, causing serious consequences of privacy leaks. To address the above problems, this paper focuses on research on privacy protection technology based on traffic obfuscation. By using traffic obfuscation technology to obscure the true traffic of smart home devices, it can prevent malicious traffic listeners from analyzing user privacy information based on traffic characteristics. We propose an enhanced smart home traffic obfuscation method called SHTObfuscator (Smart Home Traffic Obfuscator) based on the virtual user technology concept and a virtual user behavior construction method based on logical integrity. By injecting traffic fingerprints of different device activities into the real traffic environment of smart homes as obfuscating traffic, attackers cannot distinguish between the real device working status and user behavior privacy in the current home, effectively reducing the effect of traffic classification attack models. The protection level can be manually or automatically adjusted, achieving a balance between privacy protection and bandwidth overhead. The experimental results show that under the highest obfuscation level, the obfuscation method proposed in this paper can effectively reduce the classification effect of the attack model from 95% to 25%.

MapReduce is a widely used programming model for large-scale data processing. However, it still suffers from the skew problem, which refers to the case in which load is imbalanced among tasks. This problem can cause a small number of tasks to consume much more time than other tasks, thereby prolonging the total job completion time. Existing solutions to this problem commonly predict the loads of tasks and then rebalance the load among them. However, solutions of this kind often incur high performance overhead due to the load prediction and rebalancing. Moreover, existing solutions target the partitioning skew for reduce tasks, but cannot mitigate the computational skew for map tasks. Accordingly, in this paper, we present DynamicAdjust, a run-time dynamic resource adjustment technique for mitigating skew. Rather than rebalancing the load among tasks, DynamicAdjust monitors the run-time execution of tasks and dynamically increases resources for those tasks that require more computation. In so doing, DynamicAdjust can not only eliminate the overhead incurred by load prediction and rebalancing, but also culls both the partitioning skew and the computational skew. Experiments are conducted based on a 21-node real cluster using real-world datasets. The results show that DynamicAdjust can mitigate the negative impact of the skew and shorten the job completion time by up to 40.85%.

As more and more smart devices are deployed in homes, the communication between these smart home devices and elastic computing services may face some risks of privacy disclosure. Different device events (such as the camera on, video on, etc.) will generate different data traffic during communication. However, the current smart home system lacks monitoring of these device events, which may cause the disclosure of private data collected by these devices. In this paper, we present our device event monitor system, HomeMonitor. HomeMonitor runs in the OpenWRT system and supports complete event monitoring for smart home devices. HomeMoitor solves the problem that machine learning models for detecting device events do not scale flexibly. It uses the network packet size and the direction of the device event for unique identification during training. When detecting, it only needs to get the packet size and timestamp and then query the policy table for signature matching to control the device events. We evaluated the effectiveness of HomeMonitor, and the experiments show that the match rate of our method is 98.8%, the false positive rate is 1.8%, and the detection time is only 16.67% for PINBALL. The results mean that our method achieves the balance of applicable protocol scope, detection performance, and detection accuracy.