Data warehouse is an integrated repository derived from .ppt

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1、Data warehouse is an integrated repository derived from multiple distributed source databases. Created by replicating or transforming source data to new representation. Some data can be web-database or regular databases (relational, files, etc.). Warehouse creation involves reading, cleaning, aggreg

2、ating, and storing data. Warehouse data is used for strategic analysis, decision making, market research types of applications. Open access to third party users.,Basics,Examples:,Human genome databases. Drug-drug interactions database created by thousands of doctors in hundreds of hospitals. Stock p

3、rices, analyst research. Teaching material (slides, exercises, exams, examples). Census data or similar statistics collected by government.,Ideas for Security,Replication Aggregation and Generalization Exaggeration and Mutilation Anonymity User Profiles, Access Permissions,Anonymity,User privacy and

4、 warehouse data privacy. User does not know the source of data. Warehouse system does not store the results and even the access path for the query. Separation of storage system and audit query system*. Non-intrusive auditing and monitoring. Distribution of query processing, logs, auditing activity.

5、Secure multi-party computation. Mental poker (card distribution).,One can divulge information to a third party without revealing where it came from and without necessarily revealing the system has done so.,* Research project of Atallah and Prabhakar at Purdue.,Witness (Permission Inference)User can

6、execute query Q if there is an equivalent query Q for which the user has permission. Security is on result and not computation. Create views over mutually suspicious organizations by filtering out sensitive data.,Similarity Depends on Application,Two objects might be similar to a K-12 student, but n

7、ot a scientist. 1999 and 1995 annual reports of the CS department might be similar to a graduate school applicant, but not to a faculty applicant.,Similarity Based Replication*,Distinct functions used to determine how similar two objects are (Distinct Preserving Transformations). Precision: fraction

8、 of retrieved data as needed (relevant) for the user query. False Positive: object retrieved that is similar to the data needed by query, but it is not. False Negative: object is needed by the query, but not retrieved.,SOME DEFINITIONS:,* Bhargava/Annamalia, Defining Data Equivalence, IDPT, 1996,Acc

9、ess Permission*,Information permission (system-wide) (employee salary is releasable to payroll clerks and cost analyst).Physical permission (local) (cost analysts are allowed to run queries on the warehouse).,* Rosenthal & Sciore, DMDW 2000 (view security) SOL extensions.,Cooperation Instead of Auto

10、nomy in Warehouse*,In UK, the Audit Commission estimated losses of the order of $2 billion. Japanese Yakuza made a profit of $7 billion. A secure organization needs to secure data, as well as its interpretation. (Integrity of data OK, but the benefit rules were interpreted wrong and misapplied.) Int

11、erpretation Integrity,* Dhillon & Backhouse, Inf. Syst. Mgt., CACM July 2000.,Extensions to the SQL Grant/Revoke Security Model*,Limitation is a generalization of revoke. Limitation Predicates should apply to only paths (reduces chance of inadvertent & malicious denial of service). One can add eithe

12、r limitation or reactivation, or both. Limitation respects lines of authority. Flexibility can be provided to limitation.,* Rosenthal & Sciore, IFIP Conf. On Security, 2000. - Cascade Revoke, Reactivation Without Cascase, Bertino/Jajodia/Samarati, ACM TIS, 99.,Aggregation and Generalization,Summarie

13、s, Statistics (over large or small set of records) (various levels of granularity) Graphical image with numerical data. Reduce the resolution of images. Approximate answers (real-time vs. delayed quotes, blood analysis results) Inherit access to related data.,Dynamic,Authenticate users dynamically a

14、nd provides access privileges. Mobile agent interacts with the user and provides authentication and personalized views based on analysis and verification. Rule-based interaction session. Analysis of the user input. Determination of the users validity and creating a session id for the user and assign

15、ment of access permission.,Exaggeration and Misleading,Give low or high range of normal values. Initially (semantically normal). Partially incorrect or difficult to verify data. Quality improves if security is assured. Give old data, check damage done, give better data. Projected values than actual

16、values.,User Profile,User profiles are used for providing different levels of security. Each user can have a profile stored at the web server or at third party server. User can change profile attributes at run-time. User behavior is taken into account based on past record. Mobile agent accesses the

17、web page on behalf of the user and tries to negotiate with web server for the security level.,User Profile,Personal category personal identifications; name, dob, ss, etc. Data category document content; keywords document structure; audio/video, links source of data Delivery data web views, e-mail Secure data category,Static,Predefined set of user names, domain names, and access restrictions for each (restricted & inflexible) Virtual view, Materialized view, Query driven Build user profiles and represent them past behavior feedback earlier queries type, content and duration,