Developing Trustworthy Database Systems for Medical Care

Developing Trustworthy Database
Systems for Medical Care

This research is supported by CERIAS and NSF grants from ANIR & IIS.


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


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


Access Control
Auth.
Users

Access Control
Mechanism

Other
Users

Information
System

• Authorized Users
– Validated credentials
AND
– Cooperative and legitimate behavior history

• Other Users
– Lack of required credentials OR
– Non-cooperative or malicious behavior history


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)
–

Trust Enhanced 
Role­Mapping
Server

Request 
roles

users’ 
behaviors 
user

user’s trust 

trust 
information
mgmt
issuer’s trust 

Send roles
Request Access

Respond

user/issuer 
information 
database

role 
assignment

assigned 
roles

evidence
evaluation evidence 
statement,
 reliability

evidence
statement

 credential
mgmt

RBAC enhanced 
Web Server
Component implemented
Component partially 
implemented

credentials provided by 
third parties or retrieved 
from the internet

Architecture of TERM Server

role­assignment 

policies specified 
by system 
administrators


Classification Algorithm for Access Control
to Detect Malicious Users
Training Phase – Build Clusters

Classification Phase – Detect Malicious Users

Input: Training audit log record [X1, X2 ,…,Xn,  Input: cluster list, audit log record rec
Role], where X1,,…,Xn are attribute values, and  for every cluster C in cluster list
i 
Role is the role held by the user
    calculate the distance between Rec and Ci
Output: A list of centroid representations of 
find  the closest cluster Cmin
clusters  [M1, M2 ,…, Mn, pNum, Role]
if Cmin.role = Rec.role
Step 1: for every role Ri, create one cluster Ci
then return
Ci.role = Ri
        
else raise alarm
C
.
M
r
.

X
1
Experimental Study: Accuracy of Detection
i
k
k
for every attribute Mk:
r .role R
r .role R
i

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

i

• Accuracy of detection of malicious users by the
classification algorithm ranges from 60% to 90
• 90% of misbehaviors can be identified in a friendly
environment (in which fewer than 20% of behaviors
are malicious)
• 60% of misbehaviors can be identified in an
unfriendly environment (in which at least 90% of
behaviors are malicious)

Prototype TERM Server for Access Control

Defining role assignment policies

Loading evidence for role assignment

Software: />

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

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


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)


Privacy and Security of Network and
Computer 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


Information hiding
Applications 

Fraud

Privacy

Negotiation

Access control

Integrity

Data provenance 

Biometrics

Semantic web security 
Security 
Policy making

Data mining

Trust
Computer epidemic 

Anonymity 
System monitoring

Encryption 

Formal models

Network security


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


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


Research at Purdue
•
•
•

Collaboration with Dr. Clement McDonald, Regenstrief
Institute for Health Care, Indiana U. School of Medicine
Web Site: />Over one million dollars in current support from:
NSF, Cisco, Motorola, DARPA

•

Selected Publications
1.

2.
3.

4.

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.