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.
SPSC features two formats.
- Webinar — 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.
- Café — On-demand brainstorming. We meet for a café session: a tea, coffee or beverage to exchange and discuss our thoughts and ideas. Senior experts pitch a topic for an interactive debate. Naturally, a café session can be quick (about 30 minutes) but might also take sometimes up to one hour (but not longer). The day of the event is set by the presenter; we try to make it either at 10h or at 16h Brussels time.
Duration. 20 minutes talk & up to 40 minutes of discussion.
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 talk. Simply drop us an email with speaker, date/time, title & abstract to: cafe@lists.spsc-sig.org
Open to everyone. Including non-members (0 EUR fee). Please register for stating your data privacy consent and to obtain the session URL.
Events
Webinar: 2020-11-02 (Mon) Rainer Martin & Alexandru Nelus, Ruhr-Universität Bochum — 10h Brussels time [registration]
Privacy-preserving Feature Extraction and Classification in Acoustic Sensor Networks
Abstract [+]
In this talk we present a brief introduction to acoustic sensor networks and to feature extraction schemes that aim to improve the privacy vs. utility trade-off for audio classification in acoustic sensor networks. Our privacy enhancement approach consists of neural-network-based feature extraction models which aim to minimize undesired extraneous information in the feature set. To this end, we present adversarial, siamese and variational information feature extraction schemes in conjunction with neural-network-based classification (trust) and attacker (threat) models. We consider and compare schemes with explicit knowledge of the threat model and without such knowledge. For the latter, we analyze and apply the variational information approach in a smart-home scenario. It is demonstrated that the proposed privacy-preserving feature representation generalizes well to variations in dataset size and scenario complexity while successfully countering speaker identification attacks.
Webinar: 2020-12-07 (Mon) Birgit Brüggemeier, Fraunhofer Institute for Integrated Circuits IIS — 10h Brussels time [registration]
Conversational Transparency – Communicating Privacy and Security in Conversational User Interfaces
Abstract [+]
In 2019, media scandals raised awareness about privacy and security violations in Conversational User Interfaces (CUI) like Alexa, Siri and Google. Users report that they perceive CUI as “creepy” and that they are concerned about their privacy. The General Data Protection Regulation (GDPR) gives users the right to control processing of their data, for example by opting-out or requesting deletion and it gives them the right to obtain information about their data. Furthermore, GDPR advises for seamless communication of user rights, which, currently, is poorly implemented in CUI. This talk presents a data collection interface, called Chatbot Language (CBL) that we use to investigate how privacy and security can be communicated in a dialogue between user and machine. We find that conversational transparency can affect user perceptions of privacy and security positively. Moreover, user choices suggest that users are interested in obtaining information on their privacy and security in dialogue form. We discuss implications and limitations of this research.
Past events
Webinar: 2020-10-05 (Mon) Nick Gaubitch, Pindrop — 10h Brussels time [video (talk only)]
Voice Security and Why We Should Care
Abstract [+]
After a couple of decades of somewhat slow development, voice technologies have once again gained a momentum. Much of this has been driven by large leaps in speech and speaker recognition performance and consequently, the development of many voice interfaces. Some notably successful examples of current applications of voice are the Amazon Echo and Apple Siri but we also see and increasing number of institutions that make use of voice recognition to replace more traditional customer identification methods. While much of this development is exciting for speech and audio processing research, it also creates new and significant challenges in security and privacy. Furthermore, new technologies for various forms of voice modification and synthesis are on the rise which only exacerbates the problem.
In this talk we will first introduce Pindrop and the company’s mission in the world of voice security and we will take a glimpse into the global fraud landscape of call centres, which motivates the work that we do. Next, we will take a deeper dive into the specific topic of voice modification and some related research results. Finally, we will provide an outlook into the future of voice and voice security.
In this talk we will first introduce Pindrop and the company’s mission in the world of voice security and we will take a glimpse into the global fraud landscape of call centres, which motivates the work that we do. Next, we will take a deeper dive into the specific topic of voice modification and some related research results. Finally, we will provide an outlook into the future of voice and voice security.
Webinar: 2020-09-07 (Mon) Pablo Pérez Zarazaga, Aalto University — 10h Brussels time [slides, video (talk only)]
Acoustic Fingerprints for Access Management in Ad-Hoc Sensor Networks
Abstract [+]
Voice user interfaces can offer intuitive interaction with our devices, but the usability and audio quality could be further improved if multiple devices could collaborate to provide a distributed voice user interface. To ensure that users' voices are not shared with unauthorized devices, it is however necessary to design an access management system that adapts to the users' needs. Prior work has demonstrated that a combination of audio fingerprinting and fuzzy cryptography yields a robust pairing of devices without sharing the information that they record. However, the robustness of these systems is partially based on the extensive duration of the recordings that are required to obtain the fingerprint. This paper analyzes methods for robust generation of acoustic fingerprints in short periods of time to enable the responsive pairing of devices according to changes in the acoustic scenery and can be integrated into other typical speech processing tools.
Café: 2020-08-27 (Thu) Catherine Jasserand, Rijksuniversiteit Groningen — 16h Brussels time [slides]
What is speech/voice from a data privacy perspective: Insights from the GDPR
Abstract [+]
Catherine Jasserand, a postdoctoral researcher on privacy issues raised by biometric technologies, will discuss the notions of speech and voice from a data privacy perspective. If the GDPR mentions neither voice data nor speech data among the examples of personal data, it applies to both types of data when they relate to an identifiable or identified individual. The talk will be the opportunity to explain terminological issues (including what ‘identification’ means in the context of data protection).
Webinar: 2020-08-03 (Mon) Qiongxiu Li, Aalborg Universitet — 10h Brussels time [slides, video (talk only)]
Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General Framework
Abstract [+]
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. [pre-print]
Webinar: 2020-07-06 (Mon) Francisco Teixeira, INESC-ID / IST, Univ. of Lisbon — 10h Brussels time [slides, video (talk only)]
Privacy in Health Oriented Paralinguistic and Extralinguistic Tasks
Abstract [+]
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.
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.