Multi-objective Privacy-preserving Text Representation Learning

Private information can either take the form of key phrases that are explicitly contained in the text or be implicit. For example, demographic information about the author of a text can be predicted with above-chance accuracy from linguistic cues in the text itself. Letting alone its explicitness, some of the private information correlates with the output labels and therefore can be learned by a neural network. In such a case, there is a tradeoff between the utility of the representation (measured by the accuracy of the classification network) and its privacy. This problem is inherently a multi-objective problem because these two objectives may conflict, necessitating a trade-off. Thus, we explicitly cast this problem as multi-objective optimization (MOO) with the overall objective of finding a Pareto stationary solution. We, therefore, propose a multiple-gradient descent algorithm (MGDA) that enables the efficient application of the Frank-Wolfe algorithm [10] using the line search. Experimental results on sentiment analysis and part-of-speech (POS) tagging show that MGDA produces higher-performing models than most recent proxy objective approaches, and performs as well as single objective baselines.