Data and Applications Security Developments and Directions Dr. Bhavani Thuraisingham The University of Texas at Dallas Secure Object Systems February 25, 2011

Outline Background on object systems Discretionary security Multilevel security Objects for modeling secure applications Object Request Brokers Secure Object Request Brokers Secure frameworks Secure Multimedia and Geospatial Systems

Concepts in Object Database Systems Objects- every entity is an object Example: Book, Film, Employee, Car Class Objects with common attributes are grouped into a class Attributes or Instance Variables Properties of an object class inherited by the object instances Class Hierarchy Parent-Child class hierarchy Composite objects Book object with paragraphs, sections etc. Methods Functions associated with a class

Example Class Hierarchy ID Name Author Publisher Document Class D1 D2 Method1: Method2: Print-doc-att(ID) Print-doc(ID) Journal Subclass Book Subclass # of Chapters Volume # B1 J1

Example Composite Object Document Object Section 2 Object Section 1 Object Paragraph 1 Object Paragraph 2 Object

Security Issues Access Control on Objects, Classes, Attributes etc. Execute permissions on Methods Multilevel Security Security impact on class hierarchies Security impact on composite hierarchies

Objects and Security Secure OODB Secure OODA Secure DOM Persistent Design and analysis Infrastructure data store Secure OOPL Programming Secure Frameworks language Business objects Secure OOT Technologies Secure OOM Unified Object Model is Evolving

Access Control

Access Control Hierarchies

Secure Object Relational Model

Policy Enforcement

Sample Systems

Multilevel Security

Some Security Properties Security level of an instance must dominate the level of the class Security level of a subclass must dominate the level of the superclass Classifying associations between two objects Method must execute at a level that dominates the level of the method

Multilevel Secure Object Relational Systems

Sample MLS Object Systems

Objects for Secure Applications

Object Modeling

Dynamic Model

Functional Model

UML and Policies

Distributed Object Management Systems Integrates heterogeneous applications, systems and databases Every node, database or application is an object Connected through a Bus Examples of Bus include Object Request Brokers (Object Management Group) Distributed Component Object Model (Microsoft)

Object-based Interoperability Server Client Object Object Object Request Broker Example Object Request Broker: Object Management Group’s (OMG) CORBA (Common Object Request Broker Architecture)

Javasoft’s RMI (Remote Method Invocation) RMI Business Objects Clients Java-based Servers

Objects and Security Secure OODB Secure OODA Secure DOM Persistent Design and analysis Infrastructure data store Secure OOPL Programming Secure Frameworks language Business objects Secure OOT Technologies Secure OOM Unified Object Model is Evolving

Secure Object Request Brokers

CORBA (Common Object Request Broker Architecture) Security Security Service provides the following: Confidentiality Integrity Accountability Availability URLs http://www.javaolympus.com/J2SE/NETWORKING/CORBA/COR BASecurity.jsp http://student.cosy.sbg.ac.at/~amayer/projects/corbasec/sec_ov erview.html www.omg.org

OMG Security Specifications

CORBA (Common Object Request Broker Architecture) Security Security Service provides the following: Confidentiality Integrity Accountability Availability URLs http://www.javaolympus.com/J2SE/NETWORKING/CORBA/COR BASecurity.jsp http://student.cosy.sbg.ac.at/~amayer/projects/corbasec/sec_ov erview.html www.omg.org

CORBA (Common Object Request Broker Architecture) Security - 2 Identification and Authentication of Principles Authorization and Access Control Security Auditing Security of communications Administration of security information Non repudiation

Dependable Object Request Brokers Navigation Data Analysis Programming Display Consoles Data Links Processor Group (DAPG) (14) & Sensors Refresh Channels Sensor Multi-Sensor Detections Tracks Technology provided by Project Integrate Security, Real- time and Fault Tolerance Computing Future Future Future App App App Data MSI Mgmt. Data App Xchg. Infrastructure Services Real Time Operating System Hardware

Secure Frameworks

Directions Object Models UML for Security applications is becoming common practice Secure distributed object systems has gained popularity Evolution into secure object-based middleware Secure object-based languages Integrating security and real-time for object systems Distributed Objects Security cannot be an afterthought for object-based interoperability Use ORBs that have implemented security services Trends are moving towards Java based interoperability and Enterprise Application Integration (EAI) Examples of EAI products are Web Sphere (IBM) and Web Logic (BEA) Security has to be incorporated into EAI products

Why Multimedia Data Management System? Need persistent storage for managing large quantities of multimedia data A Multimedia data manager manages multimedia data such as text, images, audio, animation, video Extended by a Browser to produce a Hypermedia data management system Heterogeneity with respect to data types Numerous Applications Entertainment, Defense and Intelligence, Telecommunications, Finance, Medical

Architectures: Loose Integration User Interface Module for Integrating Data Manager with File Manager Data Manager for Metadata Multimedia File Manager Multimedia Files Metadata

Architectures: Tight Integration User Interface MM-DBMS: Integrated data manager and file manager Multimedia Database

Example: Data Model: Scenario Object A 2000 Frames Object representation Object A 2000 Frames 4/95 8/95 5/95 10/95 Object B 3000 Frames

Multimedia Data Access: Some approaches Text data Selection with index features Methods: Full text scanning, Inverted files, Document clustering Audio/Speech data Pattern matching algorithms Matching index features given for searching and ones available in the database Image data Identifying geometric boundaries, Identifying spatial relationships, Image clustering Video data Retrieval with metadata, Pattern matching with images

Metadata for Multimedia Metadata may be annotations and stored in relations I.e., Metadata from text, images, audio and video are extracted as stored as text Text metadata may be converted to relations by tagging and extracting concepts Metadata may be images of video data E.g., certain frames may be captured as metadata Multimedia data understanding Extracting metadata from the multimedia data

Storage Methods Single disk storage Objects belonging to different media types in same disk Multiple disk storage Objects distributed across disks Example: individual media types stored in different disks I.e., audio in one disk and video in another Need to synchronize for presentation (real-time techniques) Multiple disks with striping Distribute placement of media objects in different disks Called disk striping

Security Issues Access Control Multilevel Security Architecture Secure Geospatial Information Systems

Access Control for Multimedia Databases Access Control for Text, Images, Audio and Video Granularity of Protection Text John has access to Chapters 1 and 2 but not to 3 and 4 Images John has access to portions of the image Access control for pixels? Video and Audio John has access to Frames 1000 to 2000 Jane has access only to scenes in US Security constraints Association based constraints E.g., collections of images are classified

MLS Security Problem is that we may not know what may be learned from mining Can’t “Classify everything”; as some is open source or may have large benefits to being accessible This is the opposite of statistical queries – we are concerned about preventing generalities from specifics, rather then specifics from generalities – but conceptually similar. Not the same as induction – data mining finds “rules” that are generally true (high confidence and support), but not necessarily exact.

Example Security Architecture: Integrity Lock Problem is that we may not know what may be learned from mining Can’t “Classify everything”; as some is open source or may have large benefits to being accessible This is the opposite of statistical queries – we are concerned about preventing generalities from specifics, rather then specifics from generalities – but conceptually similar. Not the same as induction – data mining finds “rules” that are generally true (high confidence and support), but not necessarily exact.

Inference Control Problem is that we may not know what may be learned from mining Can’t “Classify everything”; as some is open source or may have large benefits to being accessible This is the opposite of statistical queries – we are concerned about preventing generalities from specifics, rather then specifics from generalities – but conceptually similar. Not the same as induction – data mining finds “rules” that are generally true (high confidence and support), but not necessarily exact.

Securing Geospatial Data Geospatial images could be Digital Raster Images that store images as pixels or Digital Vector Images that store images as points, lines and polygons GSAM: Geospatial Authorization Model specifies subjects, credentials, objects (e.g, points, lines, pixels etc.) and the access that subjects have to objects Reference: Authorization Model for Geospatial Data; Atluri and Chun, IEEE Transactions on Dependable and Secure Computing, Volume 1, #4, October – December 2004. Bhavani M. Thuraisingham, Gal Lavee, Elisa Bertino, Jianping Fan, Latifur Khan: Access control, confidentiality and privacy for video surveillance databases. SACMAT 2006: 1-10 Details will be given in one of the lectures after the mid-term.

Secure Geospatial Data Management References: Vijayalakshmi Atluri, Soon Ae Chun: An Authorization Model for Geospatial Data. IEEE Trans. Dependable Sec. Comput. 1(4): 238-254 (2004) Elisa Bertino, Bhavani M. Thuraisingham, Michael Gertz, Maria Luisa Damiani: Security and privacy for geospatial data: concepts and research directions. SPRINGL 2008:6- 19

Securing Geospatial Data Geospatial images could be Digital Raster Images that store images as pixels or Digital Vector Images that store images as points, lines and polygons GSAM: Geospatial Authorization Model specifies subjects, credentials, objects (e.g, points, lines, pixels etc.) and the access that subjects have to objects Reference: Authorization Model for Geospatial Data; Atluri and Chun, IEEE Transactions on Dependable and Secure Computing, Volume 1, #4, October – December 2004.

Framework for Geospatial Data Security (Joint with UCDavis and Purdue U.)

Example of several GIS repositories and GIS themes/layers for Northern California (Gertz, Bertino, Thuraisingham) Assume a single GIS data repository that manages information about parcels (being the basic units of geography for local government) and cadastre, including land use and zoning, environmental areas, and municipal utility services. Such type of repository is typically used by public sector staff to assist property owners and to support emergency, fire, and police operations. The latter type of usage includes identifying property structures and owners. Parcel maps in particular can be useful to do damage assessment after a disaster.

Example (Continued) They are also an important access point during emergencies for linking data from different GIS repositories. While such types of geospatial are used to serve the public, e.g., through Web-based interfaces, not all data layers are made publicly available. For example, property owner information is not publicly accessible A similar separation of public and private GIS data can be made for other types of themes. For example, environmental theme layers do not make information about locations of endangered species or nesting sites public. Based on this type of separation of GIS data, the following question arises: “What security mechanisms are used to specify and enforce different types of access to data in a single GIS repository?” In particular, “What provisions do GSI data managers have to (1) give public sector staff only access to GIS data relevant to their function, and (2) ensure that no sensitive geospatial data (e.g., parcel owner information) is made publicly available?” Ideally, GIS repositories should provide access control models and techniques similar to those developed for traditional (relational) databases. However, the diversity of geospatial data (feature-based versus field-based) and the complexity of feature-based geospatial data complicate a coherent and uniform access control model.

Policy Example (Bertino, Gertz, Thuraisingham) Deny/allow policies with flexible granularity, grouping mechanisms for protected objects, and space-related access restrictions. Deny/allow policies will be supported through the use of positive/negative authorizations; negative authorizations are crucial in order to support exceptions, by which, for example, an authorization is assigned to all objects in a set but one. In our context this paradigm is complicated by the larger options that we provide for denoting protected objects and by the presence of different object representations and dimensions. The main mechanism that we provide to support flexible grouping is based on the notions of object-locator and spatial window. An object-locator is a query expression that may include predicates against properties of feature types, metadata and provenance data. Predicates may also refer to topological relationships holding among the data objects, such as Within and Touches. An example of a policy using Touches is the one allowing a subject, which has access to information on a particular land parcel, to access information about all adjacent land parcels. The query expression may also include a projection component to specify an object representation and components. A spatial window is simply a spatial region in the reference space and denotes the set of object that are inside the boundary of the region. By combining such two mechanisms, one can specify sets of objects such as “all shelters occupying an area greater than 3000sf in Montgomery County”; in such case Montgomery County represents the authorization window. The use of spatial windows is particularly important to

Policy Example (Continued) Active policies. These are policies that when applied to a protected object perform certain transformations on the object, before returning it to the requester. Two relevant classes are the filtering policies and the obfuscating policies. Filtering policies refer to policies that filter out some portions of the objects before returning them to the users. These policies are directly supported by our object locator mechanisms. Obfuscating policies These policies act like filter policies except that they do not simply select objects but perform possibly complex computations on the feature(s) to be returned. Typical examples include computing a lower resolution image, and distorting some vector data (but preserving topological relationships). One can even specify policies that return incorrect data (e.g., as a honey pot in the context of misuse detection). In our model these policies are supported by the projection component, suitably extended with the possibility of invoking functions, of the object locator. We will provide a library including a variety of functions to support obfuscating policies.

Policy Example (Concluded) Context-dependent access control policies. Under such policies, information from the environment is taken into account by the access control module when taking decisions about access requests. Typical contextual information includes time and subject location. Subject location information is used to specify policies allowing a subject to access a resource only if the current location of the subject verifies certain spatial constraints. Context-dependent access policies will be supported by the introduction of a context component, as part of authorization rules, and by attribute-based specification of subjects in authorization rules. Event-based access control policies. Event-based access control policies are novel and are based on the idea that policies can be enabled/disabled depending on the occurrence of specified events. Events can include data modifications, very much like in database triggers, or application-dependent events, such as an emergency. We notice that current sensor networks and intelligent appliances make it very easy for a computer system to detect events arising in the environments. Our model will take advantage of such capabilities.

Policy Language Take existing geospatial language/model and extend for security E.g., GML Take a security model/language and extend for geospatial E.g, XACML has been extended to Geo-XACML Develop from scratch GRDF, Secure GRDF (developed at UTDallas by Alam Ashraful for PhD research)

Geospatial Semantic Web: GRDF The strength of RDF lies in the ease of composition with which RDF based formalisms can be integrated with other similar languages. On the Semantic Web, the goal is to minimize human intervention and to make way for machines to perform rule based automated reasoning. We are developing GRDF for geospatial data representation Why not use GML? - same reasons for using RDF and not XML – semantics Secure GRDF – security extensions for GRDF On the Semantic Web, the goal is to minimize human intervention and to make way for machines to perform rule based automated reasoning. In our case (i.e., for GRDF concepts), rules are codified in OWL, which is the ontology language to define terms for Semantic Web applications.

Directions Multimedia data security is getting some attention Little research on Geospatial data security Digital watermarking is getting some attention Our focus at UTD is to develop a secure geospatial semantic web We have developed a system called DAGIS and demonstrating secure interoperability Details will be given later