اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدRepairing Inconsistent XML Write-Access Control Policies
108
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
XML access control policies involving updates may contain security flaws, here called inconsistencies, in which a forbidden operation may be simulated by performing a sequence of allowed operations. This paper investigates the problem of deciding whether a policy is consistent, and if not, how its inconsistencies can be repaired. We consider policies expressed in terms of annotated DTDs defining which operations are allowed or denied for the XML trees that are instances of the DTD. We show that consistency is decidable in PTIME for such policies and that consistent partial policies can be extended to unique least-privilege consistent total policies. We also consider repair problems based on deleting privileges to restore consistency, show that finding minimal repairs is NP-complete, and give heuristics for finding repairs.
قيم البحث
اقرأ أيضاً
With the emergence of XML as a standard for representing business data, new decision support applications are being developed. These XML data warehouses aim at supporting On-Line Analytical Processing (OLAP) operations that manipulate irregular XML d
ata. To ensure feasibility of these new tools, important performance issues must be addressed. Performance is customarily assessed with the help of benchmarks. However, decision support benchmarks do not currently support XML features. In this paper, we introduce the XML Warehouse Benchmark (XWeB), which aims at filling this gap. XWeB derives from the relational decision support benchmark TPC-H. It is mainly composed of a test data warehouse that is based on a unified reference model for XML warehouses and that features XML-specific structures, and its associate XQuery decision support workload. XWeBs usage is illustrated by experiments on several XML database management systems.
XML data warehouses form an interesting basis for decision-support applications that exploit complex data. However, native-XML database management systems (DBMSs) currently bear limited performances and it is necessary to research for ways to optimiz
e them. In this paper, we propose a new join index that is specifically adapted to the multidimensional architecture of XML warehouses. It eliminates join operations while preserving the information contained in the original warehouse. A theoretical study and experimental results demonstrate the efficiency of our join index. They also show that native XML DBMSs can compete with XML-compatible, relational DBMSs when warehousing and analyzing XML data.
As tremendous amount of data being generated everyday from human activity and from devices equipped with sensing capabilities, cloud computing emerges as a scalable and cost-effective platform to store and manage the data. While benefits of cloud com
puting are numerous, security concerns arising when data and computation are outsourced to a third party still hinder the complete movement to the cloud. In this paper, we focus on the problem of data privacy on the cloud, particularly on access controls over stream data. The nature of stream data and the complexity of sharing data make access control a more challenging issue than in traditional archival databases. We present Streamforce - a system allowing data owners to securely outsource their data to the cloud. The owner specifies fine-grained policies which are enforced by the cloud. The latter performs most of the heavy computations, while learning nothing about the data. To this end, we employ a number of encryption schemes, including deterministic encryption, proxy-based attribute based encryption and sliding-window encryption. In Streamforce, access control policies are modeled as secure continuous queries, which entails minimal changes to existing stream processing engines, and allows for easy expression of a wide-range of policies. In particular, Streamforce comes with a number of secure query operators including Map, Filter, Join and Aggregate. Finally, we implement Streamforce over an open source stream processing engine (Esper) and evaluate its performance on a cloud platform. The results demonstrate practical performance for many real-world applications, and although the security overhead is visible, Streamforce is highly scalable.
Technology advances in areas such as sensors, IoT, and robotics, enable new collaborative applications (e.g., autonomous devices). A primary requirement for such collaborations is to have a secure system which enables information sharing and informat
ion flow protection. Policy-based management system is a key mechanism for secure selective sharing of protected resources. However, policies in each party of such a collaborative environment cannot be static as they have to adapt to different contexts and situations. One advantage of collaborative applications is that each party in the collaboration can take advantage of knowledge of the other parties for learning or enhancing its own policies. We refer to this learning mechanism as policy transfer. The design of a policy transfer framework has challenges, including policy conflicts and privacy issues. Policy conflicts typically arise because of differences in the obligations of the parties, whereas privacy issues result because of data sharing constraints for sensitive data. Hence, the policy transfer framework should be able to tackle such challenges by considering minimal sharing of data and support policy adaptation to address conflict. In the paper we propose a framework that aims at addressing such challenges. We introduce a formal definition of the policy transfer problem for attribute-based policies. We then introduce the transfer methodology that consists of three sequential steps. Finally we report experimental results.
XML data warehouses form an interesting basis for decision-support applications that exploit heterogeneous data from multiple sources. However, XML-native database systems currently suffer from limited performances in terms of manageable data volume
and response time for complex analytical queries. Fragmenting and distributing XML data warehouses (e.g., on data grids) allow to address both these issues. In this paper, we work on XML warehouse fragmentation. In relational data warehouses, several studies recommend the use of derived horizontal fragmentation. Hence, we propose to adapt it to the XML context. We particularly focus on the initial horizontal fragmentation of dimensions XML documents and exploit two alternative algorithms. We experimentally validate our proposal and compare these alternatives with respect to a unified XML warehouse model we advocate for.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
