1. 1 Information and Data Privacy: An Indian Perspective  Why is this important? Public concern about privacy.  Considerable concern in developed countries on the issue of using a customer’s personal information or data for intrusive and malicious purposes.  Not much importance in developing countries like India because of lack of awareness and also perceptions differ.  Concept of privacy is different in different countries and cultures.

2. 2 Introduction  Recent advances in Data Mining enable extraction of patterns about consumers based on data that is available freely on the web  Extracting meaningful and useful knowledge from consumer data is necessary to serve the consumer better, offer better services and also in some cases for security purposes  Also fraught with the risk of infringing on the consumer’s individual privacy as ‘confidential’ information about a customer may be used to discriminate against him/her.

3. 3 Objective  Review current privacy problems  Analyze the existing or stated privacy policies of some leading companies in India in the telecom, banking and insurance sectors to see if they agree and if not what are the significant differences.  Introduce the concept of Privacy Preserving Data Mining (PPDM) and describe the main approaches.  Come up with a framework to suggest which PPDM method may be applied in which domain.

4. 4 Key Findings Sector\Comp- pany AirtelVodafoneReliance TelecomPolicy exists Only company that emphasizes on the issue of sharing customers’ information outside India. Applicability of Indian privacy policies or laws in other countries where the data may be stored is a complex matter. Can have security implications.

5. 5 Key Findings(Cont.) Sector Company ICICIHDFCState Bank of India (SBI) BankingPolicy exists May use private data to protect bank's interest Does not allow sharing customers confidential information unless required by law Only bank to have a clear policy on how to limit access to customer information by their employees

6. 6 Key Findings(Cont.) Sector Company LICICICI LombardHDFC-SL InsurancePolicy exists May collect unnamed statistics which do not personally identify the user. Reserves right to perform statistical analyses but will provide only aggregated data from these analyses to third parties Log files are analyzed so that individual user is not identified. All companies can share aggregate data and overall trends without revealing individual identity HDFC_SL retains the right to share aggegated non- personally identifiable information with third parties.

7. 7 Key Recommendations Recommendations Sector TelecomData Transformation /randomization under PPDM approach BankingSecure Multiparty computaion under PPDM related methods InsuranceVertically partitioning the Data followed by a simple Data transformation

8. 8 Recommendation Justifications  In the Telecom domain companies primarily collect personal data on calling patterns and conduct surveys for planning. Customers would give share more accurate information if they knew their privacy would be protected, therefore Data transformation/randomization is proposed.  In Banking sector different parties wish to share results on joint data owned by different parties and so secure multiparty computation is suggested.

9. 9 Recommendation Justifications(Cont.)  In insurance sector one has to deal with sensitive information like private health records.  It is crucial that the personal data identifying an individual uniquely, their medical history and DNA sequences (if available) are stored such that they can not be brought together by a common user.  Vertical partitioning of the data followed by a simple transformation of the private data is therefore suggested.

10. 10 Conclusion  Policies on Information sharing are inconsistent across domains and across companies  Personal information is not always separated from public information  Policy makers in telecom, banking and insurance should be aware of privacy breaches as a result of data mining on publicly available data and therefore possible misuses.  Use of PPDM methods as suggested in appropriate domains will ensure benefits of data mining to reach the consumer without the associated pitfalls