Deep Neural Networks are increasingly being used in a variety of machine learning applications applied to user data on the cloud. However, this approach introduces a number of privacy challenges, as the cloud operator can perform secondary inferences on the available data. In this project we are targeting this challenge and try to address it with a machine learning solution, based on a specific kind of feature extraction model.
Deep Private-Feature Extraction (ArXiv)
We present and evaluate Deep Private-Feature Extractor (DPFE), a deep model which is trained and evaluated based on information theoretic constraints. Using the selective exchange of information between a user’s device and a service provider, DPFE enables the user to prevent certain sensitive information from being shared with a service provider, while allowing them to extract approved information using their model. We introduce and utilize the log-rank privacy, a novel measure to assess the effectiveness of DPFE in removing sensitive information and compare different models based on their accuracy-privacy tradeoff. We then implement and evaluate the performance of DPFE on smartphones to understand its complexity, resource demands, and efficiency tradeoffs. Our results on benchmark image datasets demonstrate that under moderate resource utilization, DPFE can achieve high accuracy for primary tasks while preserving the privacy of sensitive information.
People involved: Seyed Ali Osia, Ali Taheri, Ali Shahin Shamsabadi, Kleomenis Katevas, Hamed Haddadi, Hamid R. Rabiee
A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics (ArXiv)
Deep Neural Networks are increasingly being used in a variety of machine learning applications applied to user data on the cloud. However, this approach introduces a number of privacy and efficiency challenges, as the cloud operator can perform secondary inferences on the available data. Recently, advances in edge processing have paved the way for more efficient, and private, data processing at the source for simple tasks and lighter models, though they remain a challenge for larger, and more complicated models. In this paper, we present a hybrid approach for breaking down large, complex deep models for cooperative, privacy-preserving analytics. We do this by breaking down the popular deep architectures and fine-tune them in a suitable way. We then evaluate the privacy benefits of this approach based on the information exposed to the cloud service. We also asses the local inference cost of different layers on a modern handset for mobile applications. Our evaluations show that by using certain kind of fine-tuning and embedding techniques and at a small processing cost, we can greatly reduce the level of information available to unintended tasks applied to the data features on the cloud, and hence achieving the desired tradeoff between privacy and performance.
People involved: Seyed Ali Osia, Ali Shahin Shamsabadi, Ali Taheri, Kleomenis Katevas, Sina Sajadmanesh, Hamid R. Rabiee, Nicholas D. Lane, Hamed Haddadi
Private and Scalable Personal Data Analytics using a Hybrid Edge-Cloud Deep Learning (IEEE Computer)
Although the ability to collect, collate, and analyze the vast amount of data generated from cyber-physical systems and Internet of Things devices can be beneficial to both users and industry, this process has led to a number of challenges, including privacy and scalability issues. The authors present a hybrid framework where user-centered edge devices and resources can complement the cloud for providing privacy-aware, accurate, and efficient analytics.
people involved: Seyed Ali Osia, Ali Shahin Shamsabadi, Ali Taheri, Hamid R. Rabiee, Hamed Haddadi