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Dynamic Context-aware Access Control for Grid Applications

Dynamic Context-aware Access Control for Grid Applications
Dynamic Context-aware Access Control for Grid Applications

Dynamic Context-aware Access Control for Grid Applications?

Guangsen Zhang,Manish Parashar

The Applied Software Systems Laboratory

Department of Electrical and Computer Engineering

Rutgers University

{gszhang,parashar}@https://www.wendangku.net/doc/ca5407763.html,

Abstract

The emerging Grid infrastructure presents many chal-lenges due to its inherent heterogeneity,multi-domain char-acteristic,and highly dynamic nature.One critical chal-lenge is providing authentication,authorization and access control guarantees.In this paper,we present the SESAME dynamic context-aware access control mechanism for per-vasive Grid applications.SESAME complements current authorization mechanisms to dynamically grant and adapt permissions to users based on their current context.The un-derling dynamic role based access control(DRBAC)model extends the classic role based access control(RBAC).We also present a prototype implementation of SESAME and DRBAC with the Discover computational collaboratory and an experimental evaluation of its overheads.

Keywords:Grid security,authorization and access con-trol,context-aware,pervasive applications,Grid comput-ing.

1Introduction

Grid computing is rapidly emerging as the dominant paradigm of wide area distributed computing[1].It’s pri-mary objective is to provide a service-oriented infrastruc-ture that leverages standardized protocols and services to enable pervasive access to,and coordinated sharing of geo-graphically distributed hardware,software,and information resources.

The Grid community and the Global Grid Forum[15]are investing considerable effort in developing and deploying standard architectures and protocols that enable seamless and secure discovery,access to,and interactions among re-sources,services,and applications.This potential for seam-?The research presented in this paper is supported in part by NSF via grants numbers ACI9984357(CAREERS),EIA0103674(NGS)and EIA-0120934(ITR),and by DOE ASCI/ASAP(Caltech)via grant numbers PC295251and1052856.less aggregation,integration,and interactions has made it possible to conceive a new generation of Grid applications that are based on ad hoc,symbiotic and opportunistic in-teractions,where users,application components,Grid ser-vices,resources(systems,CPUs,instruments,storage)and data(archives,sensors)interact as peers.However,real-izing such a pervasive Grid infrastructure presents many challenges due to its inherent heterogeneity,multi-domain characteristic,and highly dynamic nature.One critical chal-lenge is providing authentication,authorization and access control guarantees.

The Grid Security Infrastructure(GSI)[5]has been ac-cepted as the primary authentication mechanism for the Grid.Developed as part of the Globus project[16],GSI de-?nes single sign-on algorithms and protocols,cross-domain authentication protocols,and temporary credentials called proxy credentials.GSI is widely used and has been inte-grated into a number of Grid environments and applications.

However,the authorization and access control challenges are not fully addressed by existing approaches.The Ak-enti[4]access control system enables multiple owners and administrators to de?ne?ne-grained usage policies in a widely distributed system.The Akenti policy engine then gathers use-conditions certi?cate de?ned by the resource owners and attribute certi?cates from the various stake hold-ers,and grants access to a resource by matching of these two certi?cates.In the Community Authorization Service (CAS)[3],resource providers grant access to a community accounts as a whole.The CAS server is designed to main-tain authorization information for all entities in the com-munity.It keeps track of?ne-grained access control infor-mation and grants restricted GSI proxy certi?cates(PCs) to community members.M.Lorch et al[6]propose a?ne grained authorization services to support ad-hoc collabo-rations using attribute certi?cates.Similarly,L.Ramakr-ishnan et al[7]present an authorization infrastructure for component-based Grid applications by providing authoriza-tion at the component interface.

While these research efforts listed above do address im-

portant aspects of the overall authorization and access con-trol problem in a Grid environment,these efforts focus on relatively static scenarios where access depends on identity of the subject.They do not address access control issues for pervasive Grid applications where the access capabili-ties and privileges of a subject not only depend on its iden-tity but also on its current context(i.e.current time,loca-tion,system resources,network state,etc.)and state.For example,consider a user accessing a remote resource or a data archive using a pervasive portal on her PDA.In such an application,the user’s access privileges depend on who she is,where she is(in a secure or insecure environment), her context(current connectivity,current load),the state of the resource or data archive she is accessing,etc.Further-more,her privileges will change as her context changes-for example,if she moves from a secure wireless link to an insecure one.Similarly,when a Grid service interacts with another service on the Grid,the access privileges of the ser-vice will also depend on the credential of the service as well as the context and state of the service,which are dynamic.

In this paper,we present the SESAME1dynamic context-aware access control mechanism for pervasive Grid applications.SESAME complements current authorization mechanisms to dynamically grant and adapt permissions to users based on their current context.The underling dynamic role based access control(DRBAC)model extends the clas-sic role based access control(RBAC)[2,8],while retaining its advantages(i.e.ability to de?ne and manage complex security policies).The model dynamically adjusts Role As-signments and Permission Assignments based on context in-formation.In DRBAC,each subject is assigned a role sub-set from the entire role set by the authority service.Simi-larly,each object has permission subsets for each role that will access it.During a secure interaction,state machines are maintained by delegated access control agents at the subject(Role State Machine)to navigate the role subset,and the object(Permission State Machine)to navigate the per-mission subset for each active role.These state machines navigate the role/permission subsets to react to changes in context and de?ne the currently active role at the subject and its assigned permissions at the object.

A prototype of SESAME and the DRBAC model has been implemented as part of the Discover[11,12]com-putational collaboratory.Discover enables geographically distributed scientists and engineers to collaboratively ac-cess,monitor and control applications,services,resources and data on the Grid using pervasive portals.The feasibility, performance and overheads of SESAME are experimentally evaluated.

The rest of this paper is organized as follows.Sec-tion2presents the SESAME dynamic access control model and describes its operation.Section3describes the pro-1Scalable,Environment Sensitive Access Management Engine totype implementation within the Discover collaboratory. Section4presents an experimental evaluation.Section5 presents a summary and conclusions.

2Dynamic Role-based Access Control As mentioned above,a key requirement for pervasive Grid applications is the support for dynamic,seamless and secure interactions between the participating entities,i.e. components,services,applications,data,instruments,re-sources and users.Guaranteeing interaction security re-quires a?ne-grained access control mechanism.Further-more,in the highly dynamic and heterogeneous Grid en-vironment,the access privileges of an entity depend on its credential,context and current state,which are dynamic.In this section,we present the SESAME Dynamic Role Based Access Control model(DRBAC)to address these require-ments.The traditional Role Base Access Control(RBAC) model is?rst discussed.The DRBAC model and its opera-tion are then described in detail.

2.1RBAC

Role based access control(RBAC)is an alternative to traditional discretionary(DAC)and mandatory access con-trol(MAC).In RBAC,users are assigned roles and roles are assigned permissions.A principle motivation behind RBAC is the ability to specify and enforce enterprise-speci?c se-curity policies in a way that maps naturally to an organi-zation’s structure.As user/role associations change more frequently then role/permission associations,in most or-ganizations,RBAC results in reduced administrative costs as compared to associating users directly with permissions. It can be shown that the cost of administrating RBAC is proportional to U+P while the cost of associating users di-rectly with permissions is proportional to U*P,where U is the number of individuals in a role and P is the number of permissions required by the role[8].Sandhu[2]de?nes a comprehensive framework for RBAC models which are characterized as follows:

?RBAC0:the basic model where users are associated with roles and roles are associated with permissions.?RBAC1:RBAC0with role hierarchies.?RBAC2:RBAC1with constraints on user/role, role/role,and/or role/permission associations.

Recently RBAC has been found to be the most attractive solution for providing security in a distributed computing infrastructure[8].Although the RBAC models vary from very simple to pretty complex,they all share the same basic structure of subject,role and privilege.Other factors,such

as relationship,time and location,which may be part of an access decision,are not considered in these models.The SESAME DRBAC model presented in this paper extends RBAC to provide context-aware access control mechanisms for dynamic and pervasive Grid applications.

2.2Dynamic Role-based Access Control Model

The formalization of the DRBAC model is based on the RBAC model presented in[9].The DRBAC model is illus-trated in Figure1.It has the following components:?USERS.A user is an entity whose access is being con-

https://www.wendangku.net/doc/ca5407763.html,ERS represents a set of users.?ROLES.A role is a job function within the context of an organization with some associated semantics re-garding the authority and responsibility conferred on the user assigned to the role.ROLES represents a set of roles.

?PERMS.A permission is an approval to access one or more DRBAC protected resources.PERMS represents

a set of permissions.

?ENVS.ENVS represents the set of context information for the system.We use an authorized“context agent”

to collect context information in our system.?SESSIONS.A session is a set of interactions between subjects and objects.SESSIONS represents a set of sessions.

?UA.UA is the mapping that assigns a role to a user.In

a session,each user is assigned a set of roles and the

context information is used to determine the active role among these.The user accesses the resource using this active role.

?PA.PA is the mapping that assign permissions to a role.Every role which has privileges to access the re-source is assigned a set of permissions and the context information is used to determine the active permissions for the roles.

In the DRBAC model,a Central Authority(CA)main-tains the overall role hierarchy for each domain.When the subject logs into the system,based on her credential and ca-pability,a subset of the role hierarchy is assigned to her for the session.The CA then sets up and delegates(using GSI) a local context agent for the subject.This agent monitors the context for the subject(using services provided by the Grid middleware)and dynamically adapts the active role. Similarly every subject maintains a set of permission hier-archies for each potential role that will access the resource.

A delegated local context agent at the subject resource

will

Figure1.The dynamic access control model use environment and state information to dynamically ad-just the permissions for each role.We formally de?ne the DRBAC model as follows:

-USERS,ROLES,PERMS,ENVS and SESSIONS (users,roles,permissions,environments and sessions, respectively).

-ACT ROLE and ACT P ERMISSION(active role and active permission respectively).

-UA?USERS×ROLES,a many-to-many mapping user-to-role assignment relation.

-PA?PERMS×ROLES,a many-to-many mapping permission-to-role assignment relation.

-Assigned roles(u:USERS,e:ENVS)→2ROLES,the mapping of user u onto a set of roles.

-Assigned permissions(r:ROLES,e:ENVS)→2P ERMS,the mapping of role r onto a set of permissions.

-User sessions(u:USERS)→2SESSIONS,the map-ping of user u onto a set of sessions.

-Session roles(s:SESSIONS)→2ROLESS,the map-ping of session s onto a set of roles.Formally: session roles(s i)?{r∈ROLES|(session roles(s i), r)∈UA}

-RH?ROLES×ROLES is a partial order on ROLES called the inheritance relation,written as≥,where r1≥r2only if all permissions of r2are also permis-sions of r1,and all users of r1are also users of r2.

-PH?PERMS×PERMS is a partial order on PERMS called the inheritance relation,written as≥,where p1≥p2only if all roles of p1are also roles of p2.

In the formal de?nitions above,UA(user assignment)de-?nes the relationship among roles,users and environments; PA(permission assignment)de?nes the relationship among

permissions,roles and environments.RH (role hierarchy)and PH (permission hierarchy)de?ne the inheritance rela-tionship among roles and permissions respectively.The fol-lowing section explains the operation of our model in detail.

2.3DRBAC Operation

In the DRBAC model,we assign each user a role subset from the entire role set.Similarly each resource will as-sign a permission subset from the entire permission set to each role that has privileges to access the resource.Figure 2shows the relationship between the role hierarchy main-tained at the Central Authority (CA)and the subset of this hierarchy assigned to a particular

user.

Figure 2.Role hierarchy state machine We use state machines at the subject (Role State Ma-chine)to maintain the role subset for a user,and at the ob-ject (Permission State Machine)to maintain the permission subset for each role.A state machine consists of state vari-ables (a role or permission)that encode state,and events that transform its state.The delegated local context agent uses middleware services to monitor context and generates events to trigger a transition of the state machine when nec-essary.

A permission hierarchy is shown in the Figure 3.Note that the null permission signi?es no access privileges.A transition is de?ned as T(Initial State,Destination State).So T(P1,P2)represents the transition from P1to P2and T(P2,P1)represents the transition from P2to P1.In this example,P2is the current active permission.Role transi-tions in the Role State Machine are similarly de?ned.

Key concerns in the implementation of the proposed state machine based access control mechanism include its performance overheads and the reliability and security of the context information.In a typical organization,the num-ber of roles and permissions is relatively small,no more than 20.As a result,with the increasing computational capability of systems,maintaining the state machine will have little if any impact on performance.Also,there are a number of research and commercial efforts [14]developing context toolkits that can provide reliable and secure context

services.

Figure 3.Permission hierarchy state machine

3

SESAME/DRBAC Prototype Implementa-tion

A prototype of SESAME and the DRBAC model has been implemented as part of the Discover [11,10]compu-tational collaboratory.Discover is a Grid-based computa-tional collaboratory that enables geographically distributed scientists and engineers to collaboratively access,monitor,and control distributed applications,services,resources and data on the Grid using pervasive portal.Key components of the Discover collaboratory include:

?Discover Collaborative Portals [11]that provide users with pervasive and collaborative access to Grid applications,services and https://www.wendangku.net/doc/ca5407763.html,ing these por-tals,users can discover and allocate resources,con?g-ure and launch applications and services,and monitor,interact with,and steer their execution.

?Discover Middleware Substrate [12,10]that enables global collaborative access to multiple,geographically distributed instances of the Discover computational collaboratory,and provides interoperability between Discover and external Grid services such as those pro-vided by Globus [16].?DIOS Interactive Object Framework (DIOS)[13]that enables the runtime monitoring,interaction and computational steering of Grid applications and ser-vices.DIOS enables application objects to be en-hanced with sensors and actuators so that they can be interrogated and controlled.An overview of the integration of SESAME and DR-BAC with Discover is presented in Figure 4.SESAME ensures the users can access,monitor and steer Grid re-sources/applications/services only if they have appropriate privileges and capabilities.As Discover portals are perva-sive and the Grid environment is dynamic,this requires dy-namic context aware access management.Note that authen-

tication services are provided by GSI [5]in our prototype

implementation.

Figure 4.Dynamic access control in discover

In our implementation,users entering the Discover col-laboratory using the portal are assigned a set of roles when they log in.A Role State Machine is then locally set up for each user,which dynamically adjusts the active role based on events from the local context agent.Similarly,the Per-mission State Machines are set up at the application (or ser-vice/resource)for each role that will access it.The Per-mission State Machines similarly adjust the active permis-sions based on events from the local context agent.The context agents are authorized by the central authority us-ing GSI delegation mechanisms.The access control policy is stored in the policy repository,which is maintained by an Authentication &Authorization Service within Discover

Middleware Substrate .Polices are speci?ed in XML and de?ne role/permission assignments and transitions as illus-trated in Figure 5.Policies de?ned for our implementa-tion include UserPolicy ,RoleHierarchyPolicy ,RoleAssign-mentPolicy ,PermissionAssignmentPolicy ,EventPolicy ,Ro-leTransitionPolicy and PermissionTransitionPolicy .

gszhangSuper UserUnsecure LinkGeneral User

Figure 5.Sample RoleTransition policy in XML

In our prototype implementation,we assume that a secu-rity administrator will guarantee the correctness of a policy for a object or subject -i.e.SESAME sets up the Role State Machines and Permission State Machines without consider-ing checking them for errors or con?icts.There are no in-herent constraints on the number of roles and permissions,or on the relationships betweens the roles or permissions.To illustrate our implementation,consider a simple exam-ple with a single user with three roles and a Grid resource with three permissions,as shown in Table 1and Table 2respectively.The role and permission hierarchies for this example are shown in Figure 6.

Table 1.Permission assignments for the ex-ample.

Role

Permissions Super User P 1,P 2,P 3Basic User P 2,P 3Guest

P 3

Table 2.Permission de?nition for the exam-ple.Permission Privileges

P 1Steer Object,View Object,Basic P 2View Object,Basic P 3

Basic

Figure 6.Role and permission hierarchies for the example.

We consider two types of context information in our im-plementation:(1)Object context such as a user’s location,time,local resource state and link state,and (2)Subject con-text,such as the current load,availability,connectivity for a

resource.Context agents build on existing Grid middleware services.For example object context can be collected using the Context Toolkit [14]and subject context can be obtained using NWS [17].

3.1SESAME/DRBAC Operation

The operation of the prototype is illustrated using a set of simple scenarios.These scenarios,although somewhat contrived,demonstrate the effectiveness and utility of the DRBAC model for Grid applications.For each of these scenarios,consider a user (say N )equipped with a mobile devices such as a PDA,and involved in collaboration sci-enti?c investigation using Discover.Assume that the user’s environment is part of the pervasive Grid environment with appropriate middleware services.

Assume that user N logs into the system using her PDA.Based on her credentials,the Authentication &Authoriza-tion service assigns her a set of roles.The Authority Ser-vice also sets up an access control agent on her PDA,which maintains the role state machine.A DRBAC policy de?ned to select an appropriate role based on the level of security of her wireless connection,i.e.her active role is Super User while the network is secure (e.g.in her laboratory or of?ce)and is Basic User if it is insecure.The corresponding Event-Policy and RoleTransitionPolicy may be de?ned as follow:-EventPolicy -Generate event insecure when N ’s link has no encryption.

-RoleTransitionPolicy -Transit role from Super User to Basic User when event insecure is generated.A corresponding permission state machine is maintained on the application side as shown in Figure 7.As seen in the ?gure each role has its own permission state machine.The dashed circle represents the current active permission for each role.A DRBAC policy is de?ned so that the active permission of the role Super User is P 1while load is low and P 2when the system load increases above some thresh-old,as there is a possibility that the application may get corrupted.The corresponding EventPolicy and Permission-TransitionPolicy may be de?ned as follow:

-EventPolicy -Generate event highload when load in-creases above Threshold .

-PermissionTransitionPolicy -Transit permission from P 1to P 2when event highload is generated.

Based on the policies de?ned above,the following sce-narios illustrate the operation of the SESAME DRBAC

model.

Figure 7.Permission hierarchy for the appli-cation

?When user N moves out of her laboratory,the context agent will detect (using middleware context services)that the wireless network no longer has the level of en-cryption required and will generate the insecure event.This event will trigger a transition in the role state ma-chine and downgrade her active role to Basic user .As a result of this transition,N will not be able to control and steer applications as she did while in her labora-tory.When she reaches her of?ce where the network is once again secure,the agent will detect this and will once again make Super User the active role.

?While in her of?ce,N ’s active role is Super User and she can monitor,interact with and steer applications under normal circumstances (load at the application server is low).However if the load on the application server increases as more users join the session,the lo-cal agent generates the highload event,which triggers a transition in the permission state machine and change from P 1to P 2.As a result Super User will no longer be able to steer the application.A screen dump from the Discover Portal during these scenarios is illustrated in Figure 8.As shown in this ?gure,due to the transitions,the portal displays “You don’t have the permission to access ....”.Note that for these scenar-ios and the experiments presented in the following section,context information was simulated.

In our current implementation of the DRBAC model,the active role of the user and the active permission of the role change independently.As a result,it is possible that even though the active role of user has been changed to match the current context,the user has certain permission(s)based on the previous role.We are currently addressing this potential consistency issue.

4Experimental Evaluation

We use the prototype implementation of SESAME in Discover to measure the overheads of the DRBAC model.

Figure8.Dynamic access control in discover The experiments were conducted on two PC using PII-200MHZ processors,running Windows NT4.0,and one PC using PIII-500MHZ processor,running RedHat Linux 7.2.The machines were connected by a100Mb Ethernet switch.The Discover Middleware was installed on the ma-chines running Windows NT4.0,while the Application was installed on the machine running RedHat Linux7.2.The Discover portal ran on the other machine running Windows NT4.0.The following factors affect overhead of the DR-BAC model.

-The number of roles assigned to the object.

-The frequency of the events(generated by the context agent at the object)that trigger transitions in the role state machine.

-The number of permissions assigned to each role.

-The frequency of the events(generated by the context agent at the subject)that trigger transitions in the per-mission state machine.

In the?rst set of experiments,we assigned each user5 roles,and the role with highest privileges had5permissions. The events that triggered transitions in the role state ma-chine were generated at different time interval.The times required to generate a request at the Discover Portal and get a response from the Applications,i.e.the interaction times, for different event frequencies are listed in Table 3.The ?rst row is for the case without DRBAC.

In the second set of experiments,we randomly generate events to trigger transitions in the role state machine and vary the number of roles assigned.The role with the highest privileges is still assigned5permissions.Table4shows the interaction times for different number of roles.

In the last set of experiments,the user had a state ma-chine with5roles and the role with the highest privileges was set as the active role.Events were randomly generated

Table3.Interaction time in ms.for different

context event frequencies.

Event frequency Time(ms.)

-2300

1min4732

2min4403

3min4102

4min3482

5min3104

Table4.Interaction time in ms.for different

number of roles.

Number of Roles Time(ms.)

-2300

52520

62608

72804

82920

93004

at the application server to trigger transitions in the permis-sion state machine.The number of permissions assigned to the active role was varied.The interaction times for differ-ent number of permissions are listed in Table5.

Table5.Interaction time in ms.for different

number of permissions.

Number of Permissions Time(ms.)

-2300

52500

62602

72698

82804

92912

These preliminary results show that in general the over-heads of the DRBAC implementation are reasonable.The primary overheads were due to the event generated by the context agent-the higher the frequency,the larger was the overhead.The context agent can be implemented as an in-dependent thread and as a result,the transition overheads at the object and subject are not signi?cant.

5Summary and Conclusions

In this paper,we presented the SESAME dynamic context-aware access control mechanism for pervasive Grid applications.SESAME complements current authorization mechanisms to dynamically grant and adapt permissions to users based on their current context.The underling dynamic role based access control(DRBAC)model extends the clas-sic role based access control(RBAC).A prototype imple-mentation of SESAME and the DRBAC model within the Discover computational collaboratory was presented.The feasibility,performance and overheads of SESAME were experimentally evaluated.The results show that the over-heads of the model are reasonable and the model can be effectively used for dynamic context-aware access control for Grid applications.

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nia,Santa Barbara,Research Project Web Site, https://www.wendangku.net/doc/ca5407763.html,/,2003.

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数据流图 二、系统功能设计 画出系统功能模块图并用文字对各功能模块进行详细介绍系统功能模块图: 三、数据库设计方案图表 1、系统E-R模型 总体E-R图: 精细化的局部E-R图: 学生借阅-归还E-R图: 管理员E-R图: 2、设计表 给出设计的表名、结构以及表上设计的完整性约束。student :

book: book_so比 borrow:存储学生的借书信息

return_table: 存储学生的归还信息 存储学生的罚单信息 man ager:

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