Reviews: Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation

The authors present a privacy-preserving protocol for learning text classifiers on short texts using secure multiparty communication (SMC). Unlike differential privacy under the central model, a more popular framework at the moment for making it difficult to distinguish the presence or absence of individuals in training data for a model, this protocol aims to ensure that a pretrained classifier may be used on new text data without leaking that data to the classifier's owner. Though the underlying classifier is not a SOTA solution to the test classification problem, hate speech detection, it is a nontrivial classifier of text and can classify a single example in a matter of seconds, substantially improving over the performance of approaches using homomorphic encryption. The authors test their approach on a collection of 10,000 tweets with binary labels describing whether they are hate speech, demonstrating the effectiveness of this tool in aiding automatic moderation of sensitive content. I want to be open that I am not an expert on SMC, and my primary knowledge of privacy-preserving ML is through differential privacy and natural language processing.