Developing Trustworthy Database Systems for Medical Care

1. Developing Trustworthy Database Systems for Medical Care • Bharat Bhargava1 (PI) • Mike Zoltowski 2, Arif Ghafoor 2, Leszek Lilien1 • 1 Department of Computer Sciences • 2 Department of Electrical and Computer Engineering • and • Center for Education and Research in Information Assurance and Security (CERIAS) • Purdue University • bb@cs.purdue.edu, {mikedz, ghafoor}@ecn.purdue.edu, llilien@cs.purdue.edu This research is supported by CERIAS and NSF grants from ANIR & IIS.

2. Security and Safety of Medical Care Environment • Objectives • Safety of patients • Safety of hospital and clinic • Security of medical databases • Issues • Medical care environments are vulnerable to malicious behavior, hostile settings, terrorism attacks, natural disasters, tampering • Reliability, security, accuracy can affect timeliness and precision of information for patient monitoring • Collaboration over networks among physicians/nurses, pharmacies, emergency personnel, law enforcement agencies, government and community leaders should be secure, private, reliable, consistent, correct and anonymous

3. Security and Safety of Medical Care Environment – cont. • Measures • Number of incidents per day in patient room, ward, or hospital • Non-emergency calls to nurses and doctors due to malfunctions, failures, or intrusions • False fire alarms, smoke detectors, pagers activation • Wrong information, data values, lost or delayed messages • Timeliness, accuracy, precision

4. Information System Access Control Mechanism Auth. Users Other Users Access Control • Authorized Users • Validated credentials AND • Cooperative and legitimate behavior history • Other Users • Lack of required credentials OR • Non-cooperative or malicious behavior history • From Yuhui • a flaw

5. user’s trust users’ behaviors assigned roles trust information mgmt role assignment evidence evaluation issuer’s trust evidence statement, reliability evidence statement user/issuer information database Trust Enhanced Role-Mapping Server Request roles user Send roles credential mgmt Request Access Respond credentials provided by third parties or retrieved from the internet role-assignment policies specified by system administrators Component implemented Component partially implemented RBAC enhanced Web Server Architecture of TERM Server Using Trust and Roles for Access Control • Approach: trust- and role-based access control • cooperates with traditional Role-Based Access Control (RBAC) • authorization based on evidence, trust, and roles (user profile analysis)

6. Classification Algorithm for Access Control to Detect Malicious Users Training Phase – Build Clusters Input: Training audit log record [X1, X2 ,…,Xn, Role], where X1,,…,Xn are attribute values, and Role is the role held by the user Output: A list of centroid representations of clusters [M1, M2 ,…, Mn, pNum, Role] Step 1: for every role Ri, create one cluster Ci Ci.role = Ri for every attribute Mk: Step 2: for every training record Reci calculate its Euclidean distance from existing clusters find the closest cluster Cmin if Cmin.role = Reci.role then reevaluate the attribute values else create new cluster Cj Cj.role = Reci.role for every attribute Mk: Cj.M k = Reci.Mk Classification Phase – Detect Malicious Users Input: cluster list, audit log record rec for every cluster Ciin cluster list calculate the distance between Rec and Ci findthe closest cluster Cmin if Cmin.role = Rec.role then return else raise alarm • Experimental Study: Accuracy of Detection • Accuracy of detection of malicious users by the classification algorithm ranges from 60% to 90 • 90% of misbehaviors can be identified in a friendlyenvironment (in which fewer than 20% of behaviors are malicious) • 60% of misbehaviors can be identified in an unfriendlyenvironment (in which at least 90% of behaviors are malicious)

7. Prototype TERM Server for Access Control Defining role assignment policies Loading evidence for role assignment Software: http://www.cs.purdue.edu/homes/bb/NSFtrust.html

8. Integrity Checking Systems • Integrity Assertions (IAs) • Predicates on values of database items • Examples • Coordinate shift in a Korean plane shot down by U.S.S.R. • IAs could have detected the error • Human error: potassium result of 3.5 reported to ICU as 8.5 • IAs caught the error • Types of IAs • Allowable value range (e.g.: K_level  [3.0, 5.5], patient_age > 16) • Relationships to values of other data (e.g.: Wishard_blood_test_results(CBC, electrol.) consistent_with Methodist_blood_test_results(CBC, electrol.) ) • Conditional value (e.g.: IF patient_on(dyzide) THEN K_trend = “decreasing”) • Triggers • For surveillance of medical data and generating suggestions for doctors

9. Privacy and Anonymity • Privacy • Protecting sensitive data from unauthorized access • Health Insurance Portability and Accountability Act (HIPAA) • patients rights to request a restriction or limitation on the disclosure of protected health information (PHI) • staff rights • Anonymity • Protecting identity of the source of data

10. Preserving Privacy and Anonymity for Information Integration - Examples • Example 1: Integration of hospital databases into research database • HospitalDB1 – Mr. Smith coded as “A” (for anonymity) • Hospital DB2 – Mr. Smith coded as “B” • Research DB12 – assure that “A” = “B” • Example 2: DB access • DB should not capture what User X did (anonymity) • User X should not know more data in DB than needed (privacy)

11. Privacy and Security of Network andComputer Systems • Integrity and correctness of data • Privacy of patient records and identification • Protect against changes to patient records or treatment plan • Protect against disabling monitoring devices, switching off/crashing computers, flawed software, disabling messages • Decrypting traffic, injection of new traffic, attacks from jamming devices

12. Information hiding Fraud Applications Privacy Negotiation Integrity Access control Data provenance Biometrics Semantic web security Security Trust Encryption Computer epidemic Policy making Anonymity Data mining Formal models System monitoring Network security

13. Emerging Technologies:Sensors and Wireless Communications • Challenge: develop sensors that detect and monitor violations in medical care environment before a threat to life occurs • Bio sensors to detect anthrax, viruses, toxins, bacteria • chips coated with antibodies that attract a specific biological agent • Ion trap mass spectrometer • aids in locating fingerprints of proteins to detect toxins or bacteria • Neutron-based detectors • detect chemical, and nuclear materials • Electronic sensors, wireless devices

14. Sensors in a Patient’s Environment • Safety and Security in Patient’s Room • Monitor the entrance and access to a patient’s room • Monitor activity patterns of devices connected to a patient • Protect patients from neglect, abuse, harm, tampering, movement outside the safety zone • Monitor visitor clothing to guarantee hygiene and prevention of infections • Safety and Security of the Hospital • Monitor temperature, humidity, air quality • Identify obstacles for mobile stretchers • Protect access to FDA controlled products, narcotics, and special drugs • Monitor tampering with medicine, fraud in prescriptions • Protect against electromagnetic attacks, power outages, and discharge of biological agents

15. Research at Purdue • Collaboration with Dr. Clement McDonald, Regenstrief Institute for Health Care, Indiana U. School of Medicine • Web Site: http://www.cs.purdue.edu/homes/bb/ • Over one million dollars in current support from: • NSF, Cisco, Motorola, DARPA • Selected Publications • B. Bhargava and Y. Zhong, "Authorization Based on Evidence and Trust", in Proc. of Data Warehouse and Knowledge Management Conference (DaWaK), Sept. 2002. • E. Terzi, Y. Zhong, B. Bhargava, Pankaj, and S. Madria, "An Algorithm for Building User-Role Profiles in a Trust Environment", in Proc. of DaWaK, Sept. 2002 . • A. Bhargava and M. Zoltowski, “Sensors and Wireless Communication for Medical Care,” in Proc. of 6th Intl. Workshop on Mobility in Databases and Distributed Systems (MDDS), Prague, Czech Republic, Sept. 2003. • B. Bhargava, Y. Zhong, and Y. Lu, "Fraud Formalization and Detection", in Proc. of DaWaK, Prague, Czech Republic, Sept. 2003.