There is an increasing trend for businesses to migrate their systems towards the cloud. Security concerns that arise when outsourcing data and computation to the cloud include data confidentiality and privacy. Given that a tremendous amount of data is being generated everyday from plethora of devices equipped with sensing capabilities, we focus on the problem of access controls over live streams of data based on triggers or sliding windows, which is a distinct and more challenging problem than access control over archival data. Specifically, we investigate secure mechanisms for outsourcing access control enforcement for stream data to the cloud. We devise a system that allows data owners to specify fine-grained policies associated with their data streams, then to encrypt the streams and relay them to the cloud for live processing and storage for future use. The access control policies are enforced by the cloud, without the latter learning about the data, while ensuring that unauthorized access is not feasible. To realize these ends, we employ a novel cryptographic primitive, namely proxy-based attribute-based encryption, which not only provides security but also allows the cloud to perform expensive computations on behalf of the users. Our approach is holistic, in that these controls are integrated with an XML based framework (XACML) for high-level management of policies. Experiments with our prototype demonstrate the feasibility of such mechanisms, and early evaluations suggest graceful scalability with increasing numbers of policies, data streams and users.
Existing works on access control for stream data assume trusted domains and focus on the specification and enforcement of access policies @cite_31 @cite_5 @cite_2 . They are different to our work which considers outsourcing access control to an untrusted environment. Similarly, previous works that use XACML for fine-grained access control have also focused on trusted domains @cite_23 . We use XACML only for policy management, and rely on encryption schemes for policy enforcement.
@cite_5 @cite_31 @cite_23 @cite_2
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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. 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 realizing 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 support 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. The management of privacy and security in the context of data stream management systems (DSMS) remains largely an unaddressed problem to date. Unlike in traditional DBMSs where access control policies are persistently stored on the server and tend to remain stable, in streaming applications the contexts and with them the access control policies on the real-time data may rapidly change. A person entering a casino may want to immediately block others from knowing his current whereabouts. We thus propose a novel ";stream-centric"; approach, where security restrictions are not persistently stored on the DSMS server, but rather streamed together with the data. Here, the access control policies are expressed via security constraints (called security punctuations, or short, sps) and are embedded into data streams. The advantages of the sp model include flexibility, dynamicity and speed of enforcement. DSMSs can adapt to not only data-related but also security-related selectivities, which helps reduce the waste of resources, when few subjects have access to data. We propose a security-aware query algebra and new equivalence rules together with cost estimations to guide the security-aware query plan optimization. We have implemented the sp framework in a real DSMS. Our experimental results show the validity and the performance advantages of our sp model as compared to alternative access control enforcement solutions for DSMSs.