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20132019
most citedDifferentially Private Bayesian Learning on Distributed Data

34 citations · 78 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SP2019★ 24 cited

Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis

Francesco Concas, Julien Mineraud, Eemil Lagerspetz +5

The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality…

stat.ML2019★ 12 cited

Privacy-preserving data sharing via probabilistic modelling

Joonas Jälkö, Eemil Lagerspetz, Jari Haukka +3

Differential privacy allows quantifying privacy loss resulting from accessing sensitive personal data. Repeated accesses to underlying data incur increasing loss. Releasing data as…

stat.ML2017★ 34 cited

Differentially Private Bayesian Learning on Distributed Data

Mikko Heikkilä, Eemil Lagerspetz, Samuel Kaski +3

Many applications of machine learning, for example in health care, would benefit from methods that can guarantee privacy of data subjects. Differential privacy (DP) has become esta…

cs.CY2016★ 7 cited

Sovereignty of the Apps: There's more to Relevance than Downloads

Stephan Sigg, Eemil Lagerspetz, Ella Peltonen +2

The value of mobile apps is traditionally measured by metrics such as the number of downloads, installations, or user ratings. A problem with these measures is that they reflect ac…

cs.CR2013★ 1 cited

The Company You Keep: Mobile Malware Infection Rates and Inexpensive Risk Indicators

Hien Thi Thu Truong, Eemil Lagerspetz, Petteri Nurmi +4

There is little information from independent sources in the public domain about mobile malware infection rates. The only previous independent estimate (0.0009%) [12], was based on…