Information Security in Business Intelligence based on Cloud: A Survey of Key Issues and the Premises of a Proposal

International audienceMore sophisticated inter-organizational interactions have generated changes in the way in which organizations make business. Advanced forms of collaborations, such as Business Process as a Service (BPaaS), allow different partners to leverage business intelligence within organizations. However, although it presents powerfull economical and technical benefits, it also arrises some pitfalls about data security, especially when it is mediated by the cloud. In this article, current aspects which have been tackled in the literature related to data risks and accountability are presented. In addition, some open issues are also presented from the analysis of the existing methodologies and techniques proposed in the literature. A final point is made by proposing an approach, which aims at preventive, detective and corrective accountability and data risk management, based on usage control policies and model driven engineering

The Abstract Accountability Language: its Syntax, Semantics and Tools

Accountability is the driving principle for several of regulatory frameworks such as the European Union's General Data Protection Regulation (EU GDPR), the Health Insurance Portability and Accountability Act (HIPAA) and the Corporate and Auditing Accountability and Responsibility Act, thus influencing how organizations run their business processes. It is a central concept for enabling trust and assurance in cloud computing and future internet-based services that may emerge. Nevertheless, accountability can have different interpretations according to the level abstraction. This leads to uncertainty concerning handling and responsibility for data in computer systems with outsourcing supply-chains, as in cloud computing. When defining policies to govern organizations, we need tools to model accountability in rich contexts, including concepts like multiple agents, obligations, remediation actions and temporal aspects. The Abstract Accountability Language (AAL) is built on logical foundations allowing to describe real-world scenarios involving accountability concerns. Its semantic principles provide us means to answer whether the conditions to reach accountability in a given context are met. Moreover, we created a tool support to verify and monitor accountability policies

MorphoSys: efficient colocation of QoS-constrained workloads in the cloud

In hosting environments such as IaaS clouds, desirable application performance is usually guaranteed through the use of Service Level Agreements (SLAs), which specify minimal fractions of resource capacities that must be allocated for unencumbered use for proper operation. Arbitrary colocation of applications with different SLAs on a single host may result in inefficient utilization of the host’s resources. In this paper, we propose that periodic resource allocation and consumption models -- often used to characterize real-time workloads -- be used for a more granular expression of SLAs. Our proposed SLA model has the salient feature that it exposes flexibilities that enable the infrastructure provider to safely transform SLAs from one form to another for the purpose of achieving more efficient colocation. Towards that goal, we present MORPHOSYS: a framework for a service that allows the manipulation of SLAs to enable efficient colocation of arbitrary workloads in a dynamic setting. We present results from extensive trace-driven simulations of colocated Video-on-Demand servers in a cloud setting. These results show that potentially-significant reduction in wasted resources (by as much as 60%) are possible using MORPHOSYS.National Science Foundation (0720604, 0735974, 0820138, 0952145, 1012798

Securing the software-defined networking control plane by using control and data dependency techniques

Software-defined networking (SDN) fundamentally changes how network and security practitioners design, implement, and manage their networks. SDN decouples the decision-making about traffic forwarding (i.e., the control plane) from the traffic being forwarded (i.e., the data plane). SDN also allows for network applications, or apps, to programmatically control network forwarding behavior and policy through a logically centralized control plane orchestrated by a set of SDN controllers. As a result of logical centralization, SDN controllers act as network operating systems in the coordination of shared data plane resources and comprehensive security policy implementation. SDN can support network security through the provision of security services and the assurances of policy enforcement. However, SDN’s programmability means that a network’s security considerations are different from those of traditional networks. For instance, an adversary who manipulates the programmable control plane can leverage significant control over the data plane’s behavior. In this dissertation, we demonstrate that the security posture of SDN can be enhanced using control and data dependency techniques that track information flow and enable understanding of application composability, control and data plane decoupling, and control plane insight. We support that statement through investigation of the various ways in which an attacker can use control flow and data flow dependencies to influence the SDN control plane under different threat models. We systematically explore and evaluate the SDN security posture through a combination of runtime, pre-runtime, and post-runtime contributions in both attack development and defense designs. We begin with the development a conceptual accountability framework for SDN. We analyze the extent to which various entities within SDN are accountable to each other, what they are accountable for, mechanisms for assurance about accountability, standards by which accountability is judged, and the consequences of breaching accountability. We discover significant research gaps in SDN’s accountability that impact SDN’s security posture. In particular, the results of applying the accountability framework showed that more control plane attribution is necessary at different layers of abstraction, and that insight motivated the remaining work in this dissertation. Next, we explore the influence of apps in the SDN control plane’s secure operation. We find that existing access control protections that limit what apps can do, such as role-based access controls, prove to be insufficient for preventing malicious apps from damaging control plane operations. The reason is SDN’s reliance on shared network state. We analyze SDN’s shared state model to discover that benign apps can be tricked into acting as “confused deputies”; malicious apps can poison the state used by benign apps, and that leads the benign apps to make decisions that negatively affect the network. That violates an implicit (but unenforced) integrity policy that governs the network’s security. Because of the strong interdependencies among apps that result from SDN’s shared state model, we show that apps can be easily co-opted as “gadgets,” and that allows an attacker who minimally controls one app to make changes to the network state beyond his or her originally granted permissions. We use a data provenance approach to track the lineage of the network state objects by assigning attribution to the set of processes and agents responsible for each control plane object. We design the ProvSDN tool to track API requests from apps as they access the shared network state’s objects, and to check requests against a predefined integrity policy to ensure that low-integrity apps cannot poison high-integrity apps. ProvSDN acts as both a reference monitor and an information flow control enforcement mechanism. Motivated by the strong inter-app dependencies, we investigate whether implicit data plane dependencies affect the control plane’s secure operation too. We find that data plane hosts typically have an outsized effect on the generation of the network state in reactive-based control plane designs. We also find that SDN’s event-based design, and the apps that subscribe to events, can induce dependencies that originate in the data plane and that eventually change forwarding behaviors. That combination gives attackers that are residing on data plane hosts significant opportunities to influence control plane decisions without having to compromise the SDN controller or apps. We design the EventScope tool to automatically identify where such vulnerabilities occur. EventScope clusters apps’ event usage to decide in which cases unhandled events should be handled, statically analyzes controller and app code to understand how events affect control plane execution, and identifies valid control flow paths in which a data plane attacker can reach vulnerable code to cause unintended data plane changes. We use EventScope to discover 14 new vulnerabilities, and we develop exploits that show how such vulnerabilities could allow an attacker to bypass an intended network (i.e., data plane) access control policy. This research direction is critical for SDN security evaluation because such vulnerabilities could be induced by host-based malware campaigns. Finally, although there are classes of vulnerabilities that can be removed prior to deployment, it is inevitable that other classes of attacks will occur that cannot be accounted for ahead of time. In those cases, a network or security practitioner would need to have the right amount of after-the-fact insight to diagnose the root causes of such attacks without being inundated with too much informa- tion. Challenges remain in 1) the modeling of apps and objects, which can lead to overestimation or underestimation of causal dependencies; and 2) the omission of a data plane model that causally links control and data plane activities. We design the PicoSDN tool to mitigate causal dependency modeling challenges, to account for a data plane model through the use of the data plane topology to link activities in the provenance graph, and to account for network semantics to appropriately query and summarize the control plane’s history. We show how prior work can hinder investigations and analysis in SDN-based attacks and demonstrate how PicoSDN can track SDN control plane attacks.Ope

D:A4.1 Socio-economic impact assessment

The executive summary ends with six concise recommendations for facilitating more accountability for data management in cloud ecosystems: 1. Provide a stronger legal base for and enforcement of data protection and accountable behavior; 2. Facilitate independent auditing of responsible data stewardship; 3. Increase public awareness of the need for accountability; 4. Balance existing information asymmetries via partnerships; 5. Focus on larger enterprises working in the public sector first, as these can serve as an example for other types of businesses; 6. Demonstrate how A4Cloud tools and mechanisms can be turned into a business model in order to encourage greater uptake and use

The privatised city:Technology and public-private partnerships in the smart city

The ‘smart city’ movement asks new questions about the role of private actors in urban governance. Smart technology providers, because of their unique position, influence policymaking through their products and services. Yet, the effect of this role on public values remains unaddressed. This article considers how the use of public-private partnerships (PPPs) in smart city development challenges public values, particularly accountability and transparency. It shows how both PPPs and smart cities frame for-profit firms as central actors in creating efficient and innovative public services and infrastructure. The risks privatisation poses for public values have to be reassessed, in light of the issue of vendor lock-in and the value-embedding capacity of technology. Furthermore, this article suggests that to mitigate such risks, data protection legislation is insufficient: the wider notion of publicisation, namely the extension of public norms to private actors acting for public purposes, needs to be re-examined in the context of the smart city. Therefore, this article contributes to the literature with a novel discussion of the possibilities and limits of using smart city PPPs as tools to safeguard public values