text: Deep Learning has recently become hugely popular in machine learning, providing significant improvements in classification accuracy in the presence of highly-structured and large databases. Researchers have also considered privacy implications of deep learning. Models are typically trained in a centralized manner with all the data being processed by the same training algorithm. If the data is a collection of users' private data, including habits, personal pictures, geographical positions, interests, and more, the centralized server will have access to sensitive information that could potentially be mishandled. To tackle this problem, collaborative deep learning models have recently been proposed where parties locally train their deep learning structures and only share a subset of the parameters in the attempt to keep their respective training sets private. Parameters can also be obfuscated via differential privacy (DP) to make information extraction even more challenging, as proposed by Shokri and Shmatikov at CCS'15. Unfortunately, we show that any privacy-preserving collaborative deep learning is susceptible to a powerful attack that we devise in this paper. In particular, we show that a distributed, federated, or decentralized deep learning approach is fundamentally broken and does not protect the training sets of honest participants. The attack we developed exploits the real-time nature of the learning process that allows the adversary to train a Generative Adversarial Network (GAN) that generates prototypical samples of the targeted training set that was meant to be private (the samples generated by the GAN are intended to come from the same distribution as the training data). Interestingly, we show that record-level DP applied to the shared parameters of the model, as suggested in previous work, is ineffective (i.e., record-level DP is not designed to address our attack).
  @cite_45: We consider the problem of producing recommendations from collective user behavior while simultaneously providing guarantees of privacy for these users. Specifically, we consider the Netflix Prize data set, and its leading algorithms, adapted to the framework of differential privacy. Unlike prior privacy work concerned with cryptographically securing the computation of recommendations, differential privacy constrains a computation in a way that precludes any inference about the underlying records from its output. Such algorithms necessarily introduce uncertainty--i.e., noise--to computations, trading accuracy for privacy. We find that several of the leading approaches in the Netflix Prize competition can be adapted to provide differential privacy, without significantly degrading their accuracy. To adapt these algorithms, we explicitly factor them into two parts, an aggregation learning phase that can be performed with differential privacy guarantees, and an individual recommendation phase that uses the learned correlations and an individual's data to provide personalized recommendations. The adaptations are non-trivial, and involve both careful analysis of the per-record sensitivity of the algorithms to calibrate noise, as well as new post-processing steps to mitigate the impact of this noise. We measure the empirical trade-off between accuracy and privacy in these adaptations, and find that we can provide non-trivial formal privacy guarantees while still outperforming the Cinematch baseline Netflix provides.
summary: Considering the security and privacy issues described above, Shokri and Shmatikov @cite_45 introduce a new collaborative learning approach, which allows participants to train their models, without explicitly sharing their training data. They exploit the fact that optimization algorithms, such as Stochastic Gradient Descent (SGD), can be parallelized and executed asynchronously. Their approach includes a selective parameter sharing process combined with local parameter updates during SGD. The participants share only a fraction of their local model gradients through a Parameter Server (PS). Each participant takes turns and uploads and downloads a percentage of the most recent gradients to avoid getting stuck into local minima. This process only works if the participants agree in advance on a network architecture @cite_45 .