Anderson Santana de Oliveira

A-PPL: An accountability policy language

Cloud Computing raises various security and privacy challenges due to the customers’ inherent lack of control over their outsourced data. One approach to encourage customers to take advantage of the cloud is the design of new accountability solutions which improve the degree of transparency with respect to data processing. In this paper, we focus on accountability policies and propose A-PPL, an accountability policy language that represents machine-readable accountability policies. A-PPL extends the PPL language by allowing customers to define additional rules on data retention, data location, logging and notification. The use of A-PPL is illustrated with a use case where medical sensors collect personal data which are then stored and processed in the cloud. We define accountability obligations related to this use case and translate them into A-PPL policies as a proof of concept of our proposal.

A Cloud Accountability Policy Representation Framework

Nowadays we are witnessing the democratization of cloud services. As a result, more and more end-users (individuals and businesses) are using these services for achieving their electronic transactions (shopping, administrative procedures, B2B transactions, etc.). In such scenarios, personal data is generally flowed between several entities and end-users need (i) to be aware of the management, processing, storage and retention of personal data, and (ii) to have necessary means to hold service providers accountable for the usage of their data. In fact, dealing with personal data raises several privacy and accountability issues that must be considered before to promote the use of cloud services. In this paper, we propose a framework for the representation of cloud accountability policies. Such policies offer to end-users a clear view of the privacy and accountability obligations asserted by the entities they interact with, as well as means to represent their preferences. This framework comes with two novel accountability policy languages. An abstract one devoted for the representation of preferences/obligations in an human readable fashion. And a concrete one for the mapping to concrete enforceable policies. We motivate our solution with concrete use case scenarios.

A Data Protection Impact Assessment Methodology for Cloud

We propose a data protection impact assessment (DPIA) method based on successive questionnaires for an initial screening and for a full screening for a given project. These were tailored to satisfy the needs of Small and Medium Enterprises (SMEs) that intend to process personal data in the cloud. The approach is based on legal and socio-economic analysis of privacy issues for cloud deployments and takes into consideration the new requirements for DPIAs within the European Union (EU) as put forward by the proposed General Data Protection Regulation (GDPR). The resultant features have been implemented within a tool.

Abstract Accountability Language

Accountability becomes a necessary principle for future computer systems. This is specially critical for the cloud and Web applications that collect personal and sensitive data from end users. Accountability regards the responsibility and liability for the data handling performed by a computer system on behalf of an organization. In case of misconduct (e.g. security breaches, personal data leaks, etc.), accountability should imply remediation and redress actions. Contrary to data privacy and access control, which is already supported by several concrete languages, there is currently no language supporting accountability clauses representation. In this work, we provide an abstract language for accountability clauses representation with temporal logic semantics.

From regulatory obligations to enforceable accountability policies in the Cloud

The widespread adoption of the cloud model for service delivery triggered several data protection issues. As a matter of fact, the proper delivery of these services typically involves sharing of personal/business data between the different parties involved in the service provisioning. In order to increase cloud consumer’s trust, there must be guarantees on the fair use of their data. Accountability provides the necessary assurance about the data governance practices to the different stakeholders involved in a cloud service chain. In this context, we propose a framework for the representation of accountability policies. Such policies offer to end-users a clear view of the privacy and accountability clauses asserted by the entities they interact with, as well as means to represent their preferences. Our framework offers two accountability policy languages: (i) an abstract language called AAL devoted for the representation of preferences/clauses in an human readable fashion, and (ii) a concrete one for the implementation of enforceable policies.

AAL and Static Conflict Detection in Policy

Security and privacy requirements in ubiquitous systems need a sophisticated policy language with features to express access restrictions and obligations. Ubiquitous systems involve multiple actors owning sensitive data concerning aspects such as location, discrete and continuous time, multiple roles that can be shared among actors or evolve over time. Policy consistency is an important problem in languages supporting these aspects. In this paper we present an abstract language (AAL) to specify most of these security and privacy features and compare it with XACML. We also classified the existing conflict detection mechanisms for XACML in dynamic, testing, or static detection. A thorough analysis of these mechanisms reveals that they have several weaknesses and they are not applicable in our context. We advocate for a classic approach using the notion of logical consistency to detect conflicts in AAL.

Track Report of Future Internet Services and Applications (FISA'2016)

The Future Internet Services and Applications (FISA) track focuses on three complementary aspects that have to be considered while setting up future Internet services: (i) their modeling, provisioning and management, (ii) data protection, and (iii) data collection, storage and analysis. FISA is in its second edition and results from the fusion of the PASCS (Privacy and Accountability for Software and Cloud Services) and PROMASC (Provisioning and Management of Service Oriented Architecture and Cloud Computing) tracks from previous WETICE editions. This report briefly presents the main topics of FISA and presents the accepted papers.

FISA 2015 Track Report: Future Internet Services and Applications

The Future Internet Services and Applications (FISA) track focuses on three complementary aspects that have to be considered while setting up future Internet services: (i) their modelling, provisioning and management, (ii) data protection, and (iii) data collection, storage and analysis. FISA is in its first edition and results from the fusion of the PASCS (Privacy and Accountability for Software and Cloud Services) and PROMASC (Provisioning and Management of Service Oriented Architecture and Cloud Computing) tracks from the previous WETICE editions. This report briefly presents the main topics of FISA and presents the accepted papers.

A4Cloud Workshop: Accountability in the Cloud

As cloud computing becomes a widely used technology, it is essential to provide mechanisms and tools that enable trust about how personal data is dealt with by cloud providers. The Cloud Accountability (A4Cloud) project tries to solve the problem of ensuring trust in the cloud by providing tools that support the process of achieving accountability. In this paper we will concentrate on some specific tools that were demonstrated and discussed during the A4Cloud workshop held in association with the IFIP Privacy Summer School in Edinburgh in 2015. In particular, we will describe tools that facilitate the appropriate choice of a cloud provider such as the Cloud Offerings Advisory Tool (COAT) and the Data Protection Impact Assessment Tool (DPIAT), tools that are in charge of controlling the data of the users such as the Data Track (DT) tool, and tools that help specify and enforce accountability related policies by using the Accountability-Primelife Policy Language (A-PPL) and an associated enforcement engine.

Track Report of Privacy and Accountability for Software and Cloud Services (PASCS 2014)

The goal of the PASCS track is to offer academic and industrial researchers and practitioners a platform for discussions related to privacy and accountability issues in software components and cloud services. For this first edition of PASCS we received 8 submissions. The program committe selected 3 long papers for presentation and publication in the WETICE proceedings on the basis of the originality, quality, and relevance to the topics of the track. Each submission received is reviewed at least by three reviewers.