Researcher on data privacy

Parra-Arnau.

interests.
data anonymization
synthetic data
private learning
differential privacy

About

My research attempts to leverage mature concepts and techniques amply utilized in information theory, convex optimization and stochastic estimation, to tackle the increasingly controversial problem of privacy in modern information systems from a perspective that is mathematically systematic and adheres to the principles of engineering optimization.

My current research interests encompass (i) the anonymization of static and dynamic data with syntactic and differential privacy guarantees; (ii) the generation of differentially-private synthetic data; and (iii) the design of mechanisms that allow machine learning algorithms to learn accurate models while protecting the privacy of the individuals on whom the data are trained.

Selected publications

Complete list
2021
Differentially private publication of database streams via hybrid video coding


Parra-Arnau, J.*, Strufe, T., Domingo-Ferrer, J.
Knowledge-Based Systems


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2021
On the privacy-utility trade-off in differentially private hierarchical text classification


Wunderlich, D., Bernau, D., Parra-Arnau, J., Aldà, F., Strufe, T. Privacy Enhancing Technologies Symposium. Undergoing revision


PDF BibTex
2020
Differentially private data publishing via cross-moment microaggregation


Parra-Arnau, J., Domingo-Ferrer, J., Soria-Comas, J. Information Fusion


PDF BibTex Code
2018
On the cost-effectiveness of mass surveillance


Parra-Arnau, J., Castelluccia, C.
IEEE Access


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2018
Optimized, direct sale of privacy in personal-data marketplaces


Parra-Arnau, J.
Information Sciences


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2018
Fine-grained control over tracking to support the ad-based Web economy


Achara, J. P., Parra-Arnau, J., Castelluccia, C.
ACM Transactions on Internet Technology


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2017
Shall I post this now? Optimized, delay-based privacy protection in social networks


Parra-Arnau, J., Gómez-Mármol, F., Rebollo-Monedero, D., Forné, J.
Knowledge & Information Systems


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2017
Pay-per-tracking: a collaborative masking model for web browsing


Parra-Arnau, J.
Information Sciences


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2017
MyAdChoices: bringing transparency and control to online advertising


Parra-Arnau, J., Achara, J., Castelluccia, C.
ACM Transactions on the Web


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2014
Optimal forgery and suppression of ratings for privacy enhancement in recommendation systems


Parra-Arnau, J., Rebollo-Monedero, D., Forné, J.
Entropy


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2014
Privacy-preserving enhanced collaborative tagging


Parra-Arnau, J., Perego, A., Ferrari, A., Forné, J., Rebollo-Monedero, D. IEEE Transactions on Knowledge and Data Engineering


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2013
On the measurement of privacy as an attacker’s estimation error


Rebollo-Monedero, D., Parra-Arnau, J., Diaz, C., Forné, J.
International Journal of Information Security


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2013
A modification of the Lloyd algorithm for k-anonymous quantization


Rebollo-Monedero, D., Forné, J., Esteve Pallarès, Parra-Arnau, J.
Information Sciences


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2012
Optimal tag suppression for privacy protection in the semantic web


Parra-Arnau, J., Rebollo-Monedero, D., Forné, J., Muñoz, J. L., Esparza, O.
Data & Knowledge Engineering


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Big private data.

We aim to pioneer advance beyond state of the art on the design of anonymization algorithms. Our ultimate goal is to contribute to making our data-driven society compatible with the right to privacy.

See Project

Cities and privacy.

The goal is to develop privacy-preserving, artificially-intelligence-based analytics for smart cities. We aim to enable stakeholders to operate on protected data while ensuring those data are useful for analytics.

See Project

Artificial data.

We aim to utilize generative adversarial networks (GANs), a recent innovation in machine learning, to design algorithms that generate synthetic location data of high quality.

See Project

d/AI/gnose OCD.

Can artificial intelligence be utilized to diagnose a mental-health disorder like the OCD just while we browse the Web? If so, that would be great for our health but not that good for our privacy.

See Project

Ads transparency.

The goal of the project is to bring transparency to online advertising and help users enforce their own particular choices over ads. With this aim we have developed MyAdChoices and MyTrackingChoices.

See Project

Recent news

Paper accepted at KBS

The paper proposes an anonymization method that can publish finite microdata streams with high protection as well as high utility. Download here.

PROPOLIS meeting

All project members have met at SAP France. Really interesting discussions on how to protect location data in smart cities. Stay tuned!

Best-paper award

Our work presented at the IEEE Conference on Dependable and Secure Computing (DSC'21) receives the best-paper award. Download here.

Sponsorship

Contact

Geb 50.34
Am Fasanengarten, 5
76131 Karlsruhe
Germany

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