@article{Bkakria2019,

title = {Optimal Distribution of Privacy Budget in Differential Privacy},

author = {Anis Bkakria and Aimilia Tasidou and Nora Cuppens-Boulahia and Fr\'{e}d\'{e}ric Cuppens and Fatma Bouattour and Feten Ben Fredj},

doi = {10.1007/978-3-030-12143-3_18},

issn = {16113349},

year  = {2019},

date = {2019-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {11391 LNCS},

pages = {222--236},

abstract = {Privacy budget management plays an important role when applying differential privacy, as it sets an upper limit in the ability to utilise the private database. In this paper, we explore the possibility of extending the total allocated privacy budget, taking into consideration the data consumer characteristics and the data utilisation context. To this end, we first study the problem of privacy budget distribution in adaptive multi-data consumer differential privacy use cases. Then, we present an extension of the classic differential privacy formal model that allows taking into consideration data consumers' information disclosure risk when distributing the privacy budget among them. Finally, we define a method that allows to optimally distribute a given privacy budget among a private database's data consumers.},

keywords = {Differential privacy, Information disclosure risk, Privacy budget distribution},

pubstate = {published},

tppubtype = {article}

}