Sharing data from various sources and of diverse kinds, and fusing them together for sophisticated analytics and mash-up applications are emerging trends, and are prerequisites for grand visions such as that of cyber-physical systems enabled smart cities. Cloud infrastructure can enable such data sharing both because it can scale easily to an arbitrary volume of data and computation needs on demand, as well as because of natural collocation of diverse such data sets within the infrastructure. However, in order to convince data owners that their data are well protected while being shared among cloud users, the cloud platform needs to provide flexible mechanisms for the users to express the constraints (access rules) subject to which the data should be shared, and likewise, enforce them effectively. We study a comprehensive set of practical scenarios where data sharing needs to be enforced by methods such as aggregation, windowed frame, value constrains, etc., and observe that existing basic access control mechanisms do not provide adequate flexibility to enable effective data sharing in a secure and controlled manner. In this paper, we thus propose a framework for cloud that extends popular XACML model significantly by integrating flexible access control decisions and data access in a seamless fashion. We have prototyped the framework and deployed it on commercial cloud environment for experimental runs to test the efficacy of our approach and evaluate the performance of the implemented prototype.
Time-series data --- similar to those considered in our paper --- could arrive at the system in continuous streams, for which relational databases such as MySQL and Postgresql are not ideal. Aurora @cite_11 is a popular data stream management system that addresses limitations of relational databases when it comes to stream data. @cite_20 @cite_7 are among the first to propose a model and implementation of access control for data streams based on Aurora. The model supports four access scenarios: column-based, value-based, general window and sliding window. Our framework supports all of these scenarios for on-demand queries over archival databases. The extension to eXACML that deals with continuous queries over stream databases is left for future work.
@cite_7 @cite_20 @cite_11
2069543922 1580709729 2149576945
Access control is an important component of any computational system. However, it is only recently that mechanisms to guard against unauthorized access for streaming data have been proposed. In this paper, we study how to enforce the role-based access control model proposed by us in [5]. We design a set of novel secure operators, that basically filter out tuples attributes from results of the corresponding (non-secure) operators that are not accessible according to the specified access control policies. We further develop an access control mechanism to enforce the access control policies based on these operators. We show that our method is secure according to the specified policies. Many data stream processing systems are increasingly being used to support applications that handle sensitive information, such as credit card numbers and locations of soldiers in battleground [1,2,3,6]. These data have to be protected from unauthorized accesses. However, existing access control models and mechanisms cannot be adequately adopted on data streams. In this paper, we propose a novel access control model for data streams based on the Aurora data model [2]. Our access control model is role-based and has the following components. Objects to be protected are essentially views (or rather queries) over data streams. We also define two types of privileges - Read privilege for operations such as Filter, Map, BSort, and a set of aggregate privileges for operations such as Min, Max, Count, Avg and Sum. The model also allows the specification of temporal constraints either to limit access to data during a given time bound or to constraint aggregate operations over the data within a specified time window. In the paper, we present the access control model and its formal semantics. Abstract.This paper describes the basic processing model and architecture of Aurora, a new system to manage data streams for monitoring applications. Monitoring applications differ substantially from conventional business data processing. The fact that a software system must process and react to continual inputs from many sources (e.g., sensors) rather than from human operators requires one to rethink the fundamental architecture of a DBMS for this application area. In this paper, we present Aurora, a new DBMS currently under construction at Brandeis University, Brown University, and M.I.T. We first provide an overview of the basic Aurora model and architecture and then describe in detail a stream-oriented set of operators.