Smart speakers and virtual assistants are part of our daily life. Their applications find not only use in our homes, where speech technology grants an unprecedented level of convenience, but also in health care, forensic sciences as well as in banking and payment methods; speech technology has dual use applications. By consequence, we need to evolve our understanding of security & privacy for applications in speech communication.
Once-a-month web seminars. The lectures range from keynote-style talks of seniors in industry and academia to practice talks of doctoral and master defenses. We try to keep the time slot for the first Monday in a month (not a bank day) at 10h Brussels time.
Duration. 40 minute talk & up to 20 minute Q&A.
Outcome. The goal of the lecture talks is to understand another perspective and discuss on particular aspects of SPSC in its inter-disciplinary setting. We need to leave our comfort zones to meaningfully anticipate the merger of speech technology with SPSC research areas including: user-interface design, study of the law, cryptography, and cognitive sciences.
Propose a lecture talk. Join the next event or simply drop us an email: firstname.lastname@example.org
Open to everyone. Including non-members.
2020-07-06 (Mon) Francisco Teixeira, INESC-ID / IST, Univ. of Lisbon — 10h Brussels time [slides]
Privacy in Health Oriented Paralinguistic and Extralinguistic Tasks
The widespread use of cloud computing applications has created a society-wide debate on how user privacy is handled by online service providers. Regulations such as the European Union's General Data Protection Regulation (GDPR), have put forward restrictions on how such services are allowed to handle user data. The field of privacy-preserving machine learning is a response to this issue that aims to develop secure classifiers for remote prediction, where both the client's data and the server's model are kept private. This is particularly relevant in the case of speech, and concerns not only the linguistic contents, but also the paralinguistic and extralinguistic info that may be extracted from the speech signal.
In this talk we provide a brief overview of the current state-of-the-art in paralinguistic and extralinguistic tasks for a major application area in terms of privacy concerns - health, along with an introduction to cryptographic methods commonly used in privacy-preserving machine learning. These will lay the groundwork for the review of the state-of-the-art of privacy in paralinguistic and extralinguistic tasks for health applications. With this talk we hope to raise awareness to the problem of preserving privacy in this type of tasks and provide an initial background for those who aim to contribute to this topic.
2020-08-03 (Mon) Qiongxiu Li, Aalborg Universitet — 10h Brussels time [registration]
Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General Framework
As the modern world becomes increasingly digitized and interconnected, distributed signal processing has proven to be effective in processing its large volume of data. However, a main challenge limiting the broad use of distributed signal processing techniques is the issue of privacy in handling sensitive data. To address this privacy issue, we propose a novel yet general subspace perturbation method for privacy-preserving distributed optimization, which allows each node to obtain the desired solution while protecting its private data. In particular, we show that the dual variables introduced in each distributed optimizer will not converge in a certain subspace determined by the graph topology. Additionally, the optimization variable is ensured to converge to the desired solution, because it is orthogonal to this non-convergent subspace. We therefore propose to insert noise in the non-convergent subspace through the dual variable such that the private data are protected, and the accuracy of the desired solution is completely unaffected. Moreover, the proposed method is shown to be secure under two widely-used adversary models: passive and eavesdropping. Furthermore, we consider several distributed optimizers such as ADMM and PDMM to demonstrate the general applicability of the proposed method. Finally, we test the performance through a set of applications. Numerical tests indicate that the proposed method is superior to existing methods in terms of several parameters like estimated accuracy, privacy level, communication cost and convergence rate.
2020-09-07 (Mon) Korbinian Riedhammer, Technische Hochschule Nürnberg — 10h Brussels time [registration]
Trusted Execution Environments for Private Speech Processing
2020-10-05 (Mon) Nick Gaubitch, Pindrop — 10h Brussels time [registration]
2020-11-02 (Mon) Rainer Martin & Alexandru Nelus, Ruhr-Universität Bochum — 10h Brussels time [TBA]
Privacy-preserving Feature Extraction and Classification in Acoustic Sensor Networks