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Secure retrieval of data requires integrity, confidentially, transparency, and metadata-privacy of the process. Existing protection mechanisms, however, provide only partially these properties: encryption schemes still expose cleartext metadata, protocols ...
The COVID-19 pandemic created a noticeable challenge to the cryptographic community with the development of contact tracing applications. The media reported a dispute between designers proposing a centralized or a decentralized solution (namely, the PEPP-P ...
Under the umbrella of smart toys, a myriad of interactive systems have addressed a variety of scenarios considering entertainment, education, sustainability, social and environmental learning through play. Tangibles and small toy robots prevail; but intera ...
Despite the rise in services generating learning analytics, there is a lack of standard models and guidelines for data integration and aggregation to inform the design choices of applications supporting learning analytics. We propose a bottom-up, user-driv ...
In this thesis, we focus on the problem of achieving practical privacy guarantees in machine learning (ML), where the classic differential privacy (DP) fails to maintain a good trade-off between user privacy and data utility. Differential privacy guarantee ...
To help fighting the COVID-19 pandemic, the Pan-European Privacy-Preserving Proximity Tracing (PEPP-PT) project proposed a Decentralized Privacy-Preserving Proximity Tracing (DP3T) system. This helps tracking the spread of SARS-CoV-2 virus while keeping th ...
The ever-growing number of edge devices (e.g., smartphones) and the exploding volume of sensitive data they produce, call for distributed machine learning techniques that are privacy-preserving. Given the increasing computing capabilities of modern edge de ...
Strategic information is valuable either by remaining private (for instance if it is sensitive) or, on the other hand, by being used publicly to increase some utility. These two objectives are antagonistic and leaking this information by taking full advant ...
Sharing data across multiple institutions for genome-wide association studies (GWAS) would enable discovery of novel genetic variants linked to health and disease. However, existing regulations on genomic data sharing and the sheer size of the data limit t ...
Decentralized algorithms for stochastic optimization and learning rely on the diffusion of information through repeated local exchanges of intermediate estimates. Such structures are particularly appealing in situations where agents may be hesitant to shar ...
