Research Directions in Identity Management

Dr. Bhavani Thuraisingham

The University of Texas at Dallas

Collaborators and co-authors of the presentation:

Prof. Latifur Khan and Prof. Murat Kantarcioglu

Students: Parveen Pallabi and Abin Chandrasekaran

The University of Texas at Dallas

Prof. Elisa Bertino

Purdue University

February 2007

Outline

Identity Management Technologies Our Research on Identity Assurance

- Policy Framework

- Data Management Framework

- Interoperability

- Coalition Data Sharing Our Research in Surveillance Our research in Biometrics and RFID

Identity Management

Biometric systems, RFID chips and other advanced identification systems have provided tools for organizations to identify and track supply chain and personnel.

Biometric identification/authentication is finding new applications such as e-passports.

Identification technologies creates unique challenges and opportunities for businesses, governments and the society with respect to security and privacy

Need better, more reliable biometric systems, fail-safe mechanisms for credential assignments and common set of best practices and standards.

Organizations using identification systems should devise systematic ways to handle associated risks

Technologies: Biometrics

Biometrics are automated methods of recognizing a person based on a physiological or behavioral characteristic

- Features measured: Face, Fingerprints, Hand geometry, handwriting, Iris, Retina and Voice

Three-steps: Capture-Process-Verification Capture: A raw biometric is captured by a sensing device such as

fingerprint scanner or video camera Process: The distinguishing characteristics are extracted from the

raw biometrics sample and converted into a processed biometric identifier record

Verification and Identification

- Matching the enrolled biometric sample against a single record; is the person really what he claims to be?

- Matching a biometric sample against a database of identifiers

Technologies: RFID

RFID (Radio Frequency Identifier) tags are transponders that can be used for identification purposes of various entities like passports, product tracking, automotive parts identification and transport payments like in highway toll tags

They are basically devices that can emit and receive radio waves within a specified region and enable the position identification of a target object.

Recent research in RFID includes

- security and privacy

- RFID data management and warehousing

Our Approach

In an RFID or biometric system, data is collected from different applications and processed, in part, in the front-end system and in-part at the back-end system (server)

The back-end access can occur over the Internet. This gives rise to a challenging end-to-end identity management problem.

We need to provide identity assurance both for the front-end and back-end subsystem as well as the network.

We envisage a system that we call an Identity Life Cycle Management System that manages information about the credential and the credential issuers.

We are focusing on Life Cycle Management System as well as the front-end and back-end systems of an RFID and/or biometrics system.

Policy Framework

Need appropriate policies that would allow administrators to set up and tailor identity assurance processes.

We are devising the required policies and developing languages to specify such policies.

We have identified two types of policies: Life Cycle management policies and Access control policies.

Life cycle management policies govern the entire identity management processes

Access control policies control the entities that access the information collected for identity purposes. We will discuss both policies.

Life Cycle Management Policies

Issuer Certification & Accreditation; What level of trust can be placed in various issuers? What level of trust can be placed in various identity credentials?

Identity Proofing & Registration: What procedures should exist to vet and issue the credential? How should individuals enroll?

Credential Creation & Issuance: Who should create electronic ID credentials? What data elements should be contained on credentials?

Credential Lifecycle Management: What if the device containing the credential is lost or stolen? What mechanisms can be used to validate the identification credential over time?

Access Control Policies

The subjects who are the users and processes that access the identity data

The objects that are the data to be protected (e.g., biometric data and RFID data).

Subject’s access to the objects is controlled by the access control policies.

These policies include policies for confidentiality, privacy, trust, data provenance and integrity.

Additional Elements of the Policies

Identification of the classes of policies relevant in the context of identity assurance and development of the corresponding policy languages. Two relevant classes include life cycle management policies and access control policies.

Development of interoperability techniques for multi-domain systems, including sharing of identity policies and information.

Development of a notion of “identity management process” that would encompass all the steps in assuring identity information flow, from policy formation and deployment, data gathering and analysis, forensics.

Identity Management for Front-end System

The front-end system reads the data, performs some processing and sends it to the backend.

One issue to be considered is the quality of data collected for identity assurance.

While techniques to support the desired level of quality of data and transactions in real-time applications have been studied, quality of data for identity management has not been considered.

Furthermore, for identity management, we need to examine data provenance as well. For example, where has the data come from? What is the history of the data? Since the identity data will be mostly used in the back-end for possibly real-time analysis, it is important to determine the impact of the quality of data on the effectiveness of the analysis.

Identity Management for Back-end System: Risk Management

A complete identity assurance solution must have a backend system to store/process necessary information, to manage risks associated with the underlying identification technologies and to enforce organizational policies.

Analyze the requirements and best practices for flexible and secure backend design that can be used with various identification technologies for financial, healthcare and defense sector applications.

Exploring risk management issues in identity assurance systems due to the potential pitfalls of underlying identification technologies.

How can the identification data stored in the backend system can be used without violating user privacy?

Identity Management for Back-end System: Data Management

RFID data share some common characteristics that we need to understand and subsequently develop an efficient RFID data management system for the backend.

RFID observations convey implicit meaning which have to be aggregated and mapped into a high level semantics.

RFID observations contain duplicate readings and /or missing readings that need to be eliminated. Finally, RFID data are temporal, streaming and in high volume which demand efficient query processing mechanism, and scalable representation of data.

Need a scalable and an adaptable data management system for RFID data. Furthermore, the system has to be secure so that unauthorized individuals do not get access to the data.

Interoperability

While standards are emerging for addressing interoperability issues for biometric systems, several features such as semantic heterogeneity have received limited attention.

Many biometric systems operate under the assumption that the data/images to be compared are obtained using the same sensor/system.

These systems may not be able to match or compare biometric data originating from different sensors.

Some progress has been made in the development of common data exchange formats to facilitate the exchange of feature sets between vendors.

Little effort has been invested in the actual development of algorithms and techniques to match these feature sets.

We are exploring the use of ontologies for specifying and reasoning about biometric data

Identity Management in a Coalition Environment

ExportData/Policy

ComponentData/Policy for Agency A

Data/Policy for Federation

ExportData/Policy

ComponentData/Policy for Agency C

ComponentData/Policy for Agency B

ExportData/Policy

Surveillance Problem Addressed

Huge amounts of surveillance and video data available in the security domain

Analysis is being done off-line usually using “Human Eyes”

Need for tools to aid human analyst ( pointing out areas in video where unusual activity occurs)

Our papers: Multimedia Tools Journal, ACM SACMAT, KDD Workshop

The Semantic Gap

The disconnect between the low-level features a machine sees when a video is input into it and the high-level semantic concepts (or events) a human being sees when looking at a video clip

Low-Level features: color, texture, shape High-level semantic concepts: presentation,

newscast, boxing match

Our Approach

Event Representation

- Estimate distribution of pixel intensity change Event Comparison

- Contrast the event representation of different video sequences to determine if they contain similar semantic event content.

Event Detection

- Using manually labeled training video sequences to classify unlabeled video sequences

Labeled Video Events

These events are manually labeled and used to classify unknown events

Walking1 Running1 Waving2

Labeled Video Events

  walking1 walking2 walking3 running1 running2 running3 running4 waving 2

walking1 0 0.27625 0.24508 1.2262 1.383 0.97472 1.3791 10.961

walking2 0.27625 0 0.17888 1.4757 1.5003 1.2908 1.541 10.581

walking3 0.24508 0.17888 0 1.1298 1.0933 0.88604 1.1221 10.231

running1 1.2262 1.4757 1.1298 0 0.43829 0.30451 0.39823 14.469

running2 1.383 1.5003 1.0933 0.43829 0 0.23804 0.10761 15.05

running3 0.97472 1.2908 0.88604 0.30451 0.23804 0 0.20489 14.2

running4 1.3791 1.541 1.1221 0.39823 0.10761 0.20489 0 15.607

waving2 10.961 10.581 10.231 14.469 15.05 14.2 15.607 0

Experiment #1

Problem: Recognize and classify events irrespective of direction (right-to-left, left-to-right) and with reduced sensitivity to spatial variations (Clothing)

“Disguised Events”- Events similar to testing data except subject is dressed differently

Compare Classification to “Truth” (Manual Labeling)

Experiment #1

Classification: Walking

Disguised Walking 1

walking1 walking2 walking3 running1 running2 running3 running4 waving2

0.97653 0.45154 0.59608 1.5476 1.4633 1.5724 1.5406 12.225

Experiment #1

Classification: Running

Disguised Running 1

walking1 walking2 walking3 running1 running2 running3 running4 waving2

1.411 1.3841 1.0637 0.56724 0.97417 0.93587 1.0957 11.629

Classifying Disguised Events

Classification: Running

Disguised Running 3

walking1 walking2 walking3 running1 running2 running3 running4 waving2

1.3049 1.0021 0.88092 0.8114 1.1042 1.1189 1.0902 12.801

Classifying Disguised Events

Classification: Waving

Disguised Waving 1

walking1 walking2 walking3 running1 running2 running3 running4 waving2

13.646 13.113 13.452 18.615 19.592 18.621 20.239 2.2451

Classifying Disguised Events

 

Disguise

walking1

Disguise

walking2

Disguise

running1

Disguise

running2

Disguise

running3

Disguise

waving1

Disguise

waving2

Disguise

walking1 0 0.19339 1.2159 0.85938 0.67577 14.471 13.429

Disguise

walking2 0.19339 0 1.4317 1.1824 0.95582 12.295 11.29

Disguise

running1 1.2159 1.4317 0 0.37592 0.45187 15.266 15.007

Disguise

Running2 0.85938 1.1824 0.37592 0 0.13346 16.76 16.247

Disguise

Running3 0.67577 0.95582 0.45187 0.13346 0 16.252 15.621

Disguise

waving1 14.471 12.295 15.266 16.76 16.252 0 0.45816

Disguise

waving2 13.429 11.29 15.007 16.247 15.621 0.45816 0

Experiment #1

This method yielded 100% Precision (i.e. all disguised events were classified correctly).

Not necessarily representative of the general event detection problem.

Future evaluation with more event types, more varied data and a larger set of training and testing data is needed

Privacy Preserving Surveillance

Raw video surveillance data

Face Detection and Face Derecognizing system

Suspicious Event Detection System

Manual Inspection of video data

Comprehensive security report listing suspicious events and people detected

Suspicious people found

Suspicious events found

Report of security personnel

Faces of trusted people derecognized to preserve privacy

Our Biometrics and RFID Research

Biometrics:

- Novel Algorithms for Face Detection and Fingerprint matching (IEEE ICTAI 2006 and ARES 2007)

RFID

- Privacy and security for the deployment of RFID.

- Secure management of RFID data management

- XML-based Traceability of RFID data

- Technical reports – submitted for publication Privacy Preserving Surveillance Working with Dallas NAFTA Association