The SEmantic Network of Data: Utility and Privacy (SENDUP) project is funded by the ANR (French national research agency). It started in November 2018 and will last 58 months.


The amount of data produced by individuals and corporations has dramatically increased during the last decades. This generalized gathering of data brings opportunities (e.g., building new knowledge using this « Big Data ») but also new privacy challenges. The general public express a growing distrust over personal data exploitation, which has been met with successive strengthened regulations (e.g. EU general data protection regulation, GDPR). In the meantime, open data is taking a crucial place within many administrations. The open data policy is a powerful move by public institutions aiming at publishing data collected by public agent. The objective is to manage this data as an asset to make it available, discoverable, and usable by anyone. Both the US and the European Community have foundations to promote this policy. This leads to an important new societal challenge at the crossroads of these social evolutions: how can privacy be preserved while publishing useful data?


Nowadays, data are often organized as graphs with an underlying semantic to allow efficient querying and support inference engines. Such is the case in, for example, linked data and semantic web typically relying on RDF. The SEND UP project focuses on such databases and will follow two main goals: (1) prevent illegitimate use of private data while querying semantic data graphs and (2) publish useful sensitive semantic data graphs will preserving privacy.

Targeted scientific contributions

A massive amount of work has focused on privacy in data presented as tables. They have resulted in multiple well-established models, such as k-anonymity, l-diversity, and differential privacy. More recently, these concepts have been translated and applied to graph representations, but mainly in the context of social networks. These methods usually consider homogeneous nodes with no semantic relation and aim at protecting the graph topology. More often than not, their utility is experimentally evaluated with regard to specific sets of functions and/or graph characteristics (e.g., diameter, max degree and degree distribution…).

To achieve semantic data graph sanitization, the SEND UP project aims at:

  • Introduce knowledge-based and usage-based utility metrics, related to facts present in, or that can be deduced from, the base. Indeed, due to the nature of the targeted graph utility metrics and evaluation can not rely on the preservation of, for example, the diameter of the graph.
  • Fully define the side-effects of transformations in semantic graph databases and introduce methods and tools to handle them. Indeed, updating instances of semantic data graphs during their sanitization implies many new difficulties including side-effects on the instances but also on their schema and constraints. The sanitization context brings issues that have been mildly studied in the literature (e.g., updating incomplete data-bases, triggering schema/constraints evolutions as side-effects of instance updates…) and even completely new ones (e.g., solving non-deterministic updates as an optimization problem regarding privacy and utility metrics).
  • Introduce new sanitization concepts granting privacy guarantees in semantic graph databases and taking into account vertex heterogeneity and the existence of logical relations and semantic rules between attributes.
  • Introduce methods and algorithms for semantic graph databases sanitization integrating new expanded anonymity concepts, usage-based and knowledge-based utility metrics but also transformations side-effects. Efficient techniques should account for side-effects during the decision process rather than merely triggering them afterward

These objectives are to be supported by a suite of software modules validated in lab implementing our proposed metrics and algorithms.