Urgent! Explainability and privacy for synthetic time series generation Career Opportunity with IRISA in France
Topic description
Financial transactions, water, gas, or electricity consumption, biomedical signals...
A vast amount of personal data is today generated in the form of timestamped sequences of data called time series hereafter.
These time series are collected and stored by companies or public organizations in order to support a large variety of usages (e.g., fraud detection in financial flows, epidemiology, smartgrid management).
They carry detailed information about individual behaviors or health status.
As a result, for obvious privacy reasons (e.g., large-scale re-identifications [5]), they are today mostly secluded within the systems that collect them, obliterating the benefits expected from large scale time series sharing.
Generative models are promising solutions.
Given an input set of time series, they generate a set of synthetic time series that is different from, but statistically close to, the input training set [3].
When protected by sound privacy-preserving mechanisms (e.g., differentially private perturbation [4]), they carry the promise to enable organizations to share (synthetic) time series at a large scale without jeopardizing privacy guarantees.
However, utility of synthetic time series is both complex and hard to achieve, especially when strong privacy guarantees, e.g., differential privacy, are met.
First no generative model consistently outperforms the others on all the datasets or on all the utility metrics.
Second, within a set of synthetic time series, some time series might exhibit punctual anomalies.
As a result, time series generative models need to be able to explain their outputs both globally and locally in order to ensure their validity, to understand the sources of errors, and eventually to allow reliable usages.
While there exists a rich litterature studying explainability techniques for classifiers (e.g., [2]) the issue of explaining generative models has largely been ignored until very recently.
The need to provide differential privacy guarantees further complicates the issue because the privacy guarantees must cover the explainability algorithms in addition to the generative model, and they require to inject possibly large random perturbations at training time, introducing additional variance in the results.
The goal of this PhD thesis is to design, implement, and thoroughly evaluate explainability techniques for synthetic time-series generation algorithms with differentially private guarantees.
The main tasks of the PhD student will be to:
Funding category
Public funding alone (i.e. government, region, European, international organization research grant)Funding further details
Chaire CPDDF (Fondation Univ.✨ Smart • Intelligent • Private • Secure
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Unlock Your Explainability and Potential: Insight & Career Growth Guide
Real-time Explainability and Jobs Trends in France, France (Graphical Representation)
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Great news! IRISA is currently hiring and seeking a Explainability and privacy for synthetic time series generation to join their team. Feel free to download the job details.
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