Section: New Results
Privacy and Machine Learning
Private Protocols for U-Statistics in the Local Model and Beyond In , we study the problem of computing -statistics of degree 2, i.e., quantities that come in the form of averages over pairs of data points, in the local model of differential privacy (LDP). The class of -statistics covers many statistical estimates of interest, including Gini mean difference, Kendall's tau coefficient and Area under the ROC Curve (AUC), as well as empirical risk measures for machine learning problems such as ranking, clustering and metric learning. We first introduce an LDP protocol based on quantizing the data into bins and applying randomized response, which guarantees an -LDP estimate with a Mean Squared Error (MSE) of under regularity assumptions on the -statistic or the data distribution. We then propose a specialized protocol for AUC based on a novel use of hierarchical histograms that achieves MSE of for arbitrary data distribution. We also show that 2-party secure computation allows to design a protocol with MSE of , without any assumption on the kernel function or data distribution and with total communication linear in the number of users . Finally, we evaluate the performance of our protocols through experiments on synthetic and real datasets.
Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion? In , we study Automatic Speech Recognition (ASR), a key technology in many services and applications. This typically requires user devices to send their speech data to the cloud for ASR decoding. As the speech signal carries a lot of information about the speaker, this raises serious privacy concerns. As a solution, an encoder may reside on each user device which performs local computations to anonymize the representation. In this paper, we focus on the protection of speaker identity and study the extent to which users can be recognized based on the encoded representation of their speech as obtained by a deep encoder-decoder architecture trained for ASR. Through speaker identification and verification experiments on the Librispeech corpus with open and closed sets of speakers, we show that the representations obtained from a standard architecture still carry a lot of information about speaker identity. We then propose to use adversarial training to learn representations that perform well in ASR while hiding speaker identity. Our results demonstrate that adversarial training dramatically reduces the closed-set classification accuracy, but this does not translate into increased open-set verification error hence into increased protection of the speaker identity in practice. We suggest several possible reasons behind this negative result.
Evaluating Voice Conversion-based Privacy Protection against Informed Attackers Speech signals are a rich source of speaker-related information including sensitive attributes like identity or accent. With a small amount of found speech data, such attributes can be extracted and modeled for malicious purposes like voice cloning, spoofing, etc. In , we investigate speaker anonymization strategies based on voice conversion. In contrast to prior evaluations, we argue that different types of attackers can be defined depending on the extent of their knowledge about the conversion scheme. We compare two frequency warping-based conversion methods and a deep learning based method in three attack scenarios. The utility of the converted speech is measured through the word error rate achieved by automatic speech recognition, while privacy protection is assessed by state-of-the-art speaker verification techniques (i-vectors and x-vectors). Our results show that voice conversion schemes are unable to effectively protect against an attacker that has extensive knowledge of the type of conversion and how it has been applied, but may provide some protection against less knowledgeable attackers.