Differential privacy for collaborative security

35 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing (2014, neutral): “However, others have motivated or designed DP mechanisms with the goal of ensuring the privacy of patients’ diseases [15], features on users’ social network profiles [33], and website visits in network traces [38]— all of which relate to attribute privacy.”
Differentially-private network trace analysis (2010, methodology): “Reed et al [25] recently proposed an analysis language similar to PINQ to detect botnets in a di.erentially private manner.”
Shredder: Learning Noise Distributions to Protect Inference Privacy (2019, background): “This definition gives a robust mathematical guarantee to the question of – given a private training set (or, database entry) as input, how safe is the trained model (or, aggregate database) to publish [72].”
Collaborative Security (2015, methodology): “These are examples of explicit privacy, which is mentioned by many in the literature [Barkan et al. 2003; Schmidt et al. 2009; Enck et al. 2010; Reed et al. 2010; Schlegel et al. 2011; Arapinis et al. 2012; Grace et al. 2012].”
Proving programs robust (2011, background): “Reed and Pierce [25] have since given a type system that can verify the Lipschitz-continuity of functional programs, as a component of a new language for differential privacy [24].”