Federated Learning (FL) is a promising technique for edge computing environments as it provides better data privacy protection. It enables each edge node in the system to send a central server a computed value, named gradient, rather than sending raw data. However, recent research results show that the FL is still vulnerable to an inference attack, which is an adversarial algorithm that is capable of identifying the data used to compute the gradient. One prevalent mitigation strategy is differential privacy which computes a gradient with noised data, but this causes another problem that is accuracy degradation. To effectively deal with this problem, this paper proposes a new digestive neural network (DNN) and integrates it into FL. The proposed scheme distorts raw data by DNN to make it unrecognizable then computes a gradient by a classification network. The gradients generated by edge nodes will be sent to the server to complete a trained model. The simulation results show that the proposed scheme has 9.31% higher classification accuracy and 19.25% lower attack accuracy on average than the differential private schemes.

Federated Learning (FL) is an efficient and secure machine learning technique designed for decentralized computing systems such as fog and edge computing. Its learning process employs frequent communications as the participating local devices send updates, either gradients or parameters of their models, to a central server that aggregates them and redistributes new weights to the devices. In FL, private data does not leave the individual local devices, and thus, rendered as a robust solution in terms of privacy preservation. However, the recently introduced membership inference attacks pose a critical threat to the impeccability of FL mechanisms. By eavesdropping only on the updates transferring to the center server, these attacks can recover the private data of a local device. A prevalent solution against such attacks is the differential privacy scheme that augments a sufficient amount of noise to each update to hinder the recovering process. However, it suffers from a significant sacrifice in the classification accuracy of the FL. To effectively alleviate the problem, this paper proposes a Digestive Neural Network (DNN), an independent neural network attached to the FL. The private data owned by each device will pass through the DNN and then train the FL. The DNN modifies the input data, which results in distorting updates, in a way to maximize the classification accuracy of FL while the accuracy of inference attacks is minimized. Our simulation result shows that the proposed DNN shows significant performance on both gradient sharing- and weight sharing-based FL mechanisms. For the gradient sharing, the DNN achieved higher classification accuracy by 16.17% while 9% lower attack accuracy than the existing differential privacy schemes. For the weight sharing FL scheme, the DNN achieved at most 46.68% lower attack success rate with 3% higher classification accuracy.

MPEG-DASH is a video streaming standard that outlines protocols for sending audio and video content from a server to a client over HTTP. However, it creates an opportunity for an adversary to invade users’ privacy. While a user is watching a video, information is leaked in the form of meta-data, the size of data and the time the server sent the data to the user. After a fingerprint of this data is created, the adversary can use this to identify whether a target user is watching the corresponding video. Only one defense strategy has been proposed to deal with this problem: differential privacy that adds sufficient noise in order to muddle the attacks. However, that strategy still suffers from the trade-off between privacy and efficiency. This paper proposes a novel defense strategy against the attacks with rigorous privacy and performance goals creating a private, scalable solution. Our algorithm, “No Data are Alone” (NDA), is highly efficient. The experimental results show that our scheme is more than two times efficient in terms of excess downloaded video (represented as waste) compared to the most efficient differential privacy-based scheme. Additionally, no classifier can achieve an accuracy above 7.07% against videos obfuscated with our scheme.