DATA MINING - 1DL105, 1DL025

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1 DATA MINING - 1DL105, 1DL025 Fall 2009 An introductory class in data mining Kjell Orsborn Uppsala Database Laboratory Department of Information Technology, Uppsala University, Uppsala, Sweden 12/17/09 1

2 Introduction to Data Mining Privacy in Data mining (slides and selected papers) Kjell Orsborn Department of Information Technology Uppsala University, Uppsala, Sweden 12/17/09 2

3 Privacy and security in data mining Protecting private data is an important concern for society Several laws now require explicit consent prior to analysis of an individual s data However, its importance is not limited to individuals Corporations might also need to protect their information s privacy, even though sharing it for analysis could benefit the company. Clearly, the trade-off between sharing information for analysis and keeping it secret to preserve corporate trade secrets and customer privacy is a growing challenge 12/17/09 3

4 Techniques for privacy and security Most data mining applications operate under the assumption that all the data is available at a single central repository, called a data warehouse. This poses a huge privacy problem because violating only a single repository s security exposes all the data. A naive solution to the problem is de-identification remove all identifying information from the data and release it pinpointing exactly what constitutes identification information is difficult Worse, even if de-identification is possible and (legally) acceptable, it s extremely hard to do effectively without losing the data s utility. Studies have used externally available public information to re-identify anonymous data and proved that effectively anonymizing the data required removal of substantial detail. Another solution is to avoid centralized warehouses Requires specialized distributed data mining algorithms, e.g. Secure multiparty computation Accurate methods shown for classification and association analysis A third approach is data perturbation i.e. modifying data so that it no longer represents real individuals. 12/17/09 4

5 Distributed data mining The way the data is distributed also plays an important role in defining the problem because data can be partitioned into many parts either vertically or horizontally. Vertical partitioning of data implies that although different sites gather information about the same set of entities, they collect different feature sets. Banks, for example, collect financial transaction information, whereas the IRS collects tax information. Figure 2 illustrates vertical partitioning and the kind of useful knowledge we can extract from it. The figure describes two databases, one containing individual medical records and another containing cell-phone information for the same set of people. Mining the joint global database might reveal such information as cell phones with Li/Ion batteries can lead to brain tumors in diabetics. 12/17/09 5

6 Distributed data mining In horizontal partitioning, different sites collect the same set of information but about different entities. Different supermarkets, for example, collect the same type of grocery shopping data. Figure 3 illustrates horizontal partitioning and shows the credit-card databases of two different (local) credit unions. Taken together, we might see that fraudulent customers often have similar transaction histories. However, no credit union has sufficient data by itself to discover the patterns of fraudulent behavior. 12/17/09 6

7 Secure distributed computation The secure sum protocol is a simple example of a (information theoretically) secure multiparty computation. Site k generates a random number R uniformly chosen from [0.. n], adds this to its local value x k, and then sends the sum R + x k (mod n) to site k+ 1 (mod l). Drawback of SMC is inefficiency and complexity of model 12/17/09 7

8 Statistical database security Databases often include sensitive information about single individuals that must be protected from unallowed use. However, statistical information should be extractable from the database. Statistical database security must prohibit access of individual data elements. Three main security mechanisms: conceptual, restriction-based, and perturbation-based. Examples: prohibit queries on attribute level only queries for statistical aggregation (statistical queries) statistical queries are prohibited when the selection from the population is to small. prohibit repeated statistical queries on the same tuples. introduce distortion into data. 12/17/09 8

9 Security in statistical databases Statistical database security, (also called inference control), should prevent and avoid possibilities to infer protected information from the set of allowed and fully legitimate statistical queries (statistical aggregation). A security problem occur when providing statistical information without requiring to release sensitive information concerning individuals. The main problem with SDB security is to accomplish a good compromise between integrity for individuals and the need for knowledge and information management and analysis of organizations. 12/17/09 9

10 Inference protection techniques One can divide inference protection techniques into three main categories: conceptual, restriction-based, and perturbation-based techniques. Conceptual techniques: Treats the security problem on a conceptual level lattice model conceptual partitioning 12/17/09 10

11 Inference protection techniques Restriction-based techniques Prevent queries for certain types of statistical queries query-set size control expanded query-set size control query-set overlap control audit-based control 12/17/09 11

12 Inference protection techniques Perturbation-based techniques Modifies information that is stored or presented data swapping random-sample queries fixed perturbation query-based perturbation rounding (systematic, random, controlled) 12/17/09 12