Propagation of Policies in Rich Data Flows

Abstract

Governing the life cycle of data on the web is a challenging issue for organisations and users. Data is distributed under certain policies that determine what actions are allowed and in which circumstances. Assessing what policies propagate to the output of a process is one crucial problem. Having a description of policies and data flow steps implies a huge number of propagation rules to be specified and computed (number of policies times number of actions). In this paper we provide a method to obtain an abstraction that allows to reduce the number of rules significantly. We use the Datanode ontology, a hierarchical organisation of the possible relations between data objects, to compact the knowledge base to a set of more abstract rules. After giving a definition of Policy Propagation Rule, we show (1) a methodology to abstract policy propagation rules based on an ontology, (2) how effective this methodology is when using the Datanode ontology, (3) how this ontology can evolve in order to better represent the behaviour of policy propagation rules.

Propagating Data Policies

“…A discussion on ODRL action dependencies and how they affect the policy semantics is included in [13]. Nonetheless, to the best of our knowledge, the first attempt to analyse how policies can propagate in manipulation processes is the one presented in one of our earlier papers [6]. In [6] we introduced the notion of Policy Propagation Rule (PPR) in order to solve the task of automatically deciding what policies associated to a data source need to be enforced to the output of a process in which that data source is involved.…”

“…. $Priceless https://doi.org/10.1145/nnnnnnn.nnnnnnn conditions applicable to its output [6]. For example, in a process producing aggregated statistics from two different input datasets, should the duty to attribute the source of the data or restrictions on commercial applications still apply to the result?…”

An Incremental Learning Method to Support the Annotation of Workflows with Data-to-Data Relations

“…Research on process formalization and description covers a variety of aspects, from the problem of reproducibility to the ones of validation, preservation, tracing and decay [20,7,11,22,3]. Several models have been proposed for describing workflow executions, like the W3C PROV Model 5 , the Provenance Model for Workflows (OPMW) 6 and more recently the Publishing Workflow Ontology (PWO) 7 introduced in [9]. A recent line of research is focused on understanding the activities behind processes in workflows, with the primary objective to support preservation and reusability of workflow components, particularly in the context of scientific workflows [2,10].…”

“…Instead on characterizing the activities of a workflow (like in [10]), Datanode can be applied to describe it in terms of relations between the input and the output of processors 12 . The resulting network of data objects can be used to reason upon the propagation of policies, for example in the context of a Smart City data hub [6,4].…”

“…Recently, a data-centric approach for the representation of data relying systems has been proposed with the aim to simulate the impact of process executions on the data involved, particularly to perform reasoning on the propagation of data policies [6,4]. This approach puts the data objects as first class citizens, aiming to represent the possible semantic relations among the data involved.…”