From where you go to what you say to Siri and Google Assistant, most smartphone apps collect your data continuously. Companies then sell this data to advertising companies, hence why it can sometimes feel like you are recommended ads about products you mentioned in passing once. Data privacy advocate Gaël Duval said that, thankfully, it's possible to change settings so this doesn't happen. Murena believes this has measurable benefits: he says that poor data privacy and personalised adverts directly contribute to increased time spent online, impulse buying and even worsening mental health problems – as tech companies understand more about you, they will target adverts at you more precisely. Research by TASO in 2022 found that 79 percent of people were worried about online technology companies using their data, and 65 percent felt uncomfortable sharing their data to use services for free.

Denoising, detrending, deconvolution: usual restoration tasks, traditionally decoupled. Coupled formulations entail complex ill-posed inverse problems. We propose PENDANTSS for joint trend removal and blind deconvolution of sparse peak-like signals. It blends a parsimonious prior with the hypothesis that smooth trend and noise can somewhat be separated by low-pass filtering. We combine the generalized quasi-norm ratio SOOT/SPOQ sparse penalties $\ell_p/\ell_q$ with the BEADS ternary assisted source separation algorithm. This results in a both convergent and efficient tool, with a novel Trust-Region block alternating variable metric forward-backward approach. It outperforms comparable methods, when applied to typically peaked analytical chemistry signals. Reproducible code is provided.