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Developing Trustworthy Database Systems for Medical Care includes about Security and Safety of Medical Care Environment; Access Control; Using Trust and Roles for Access Control; Classification Algorithm for Access Control to Detect Malicious Users.
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 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: http://www.cs.purdue.edu/homes/bb/NSFtrust.html 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: 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 ...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... Encryption Formal models Network security Emerging Technologies: Sensors and Wireless Communications • Challenge: develop sensors that detect and monitor violations in medical care environment before... "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