Name
Abstract | ||
|---|---|---|
Two organizations that have records on the same collection of individuals can benefit from sharing attributes on these individuals. The combined data, with records linked on certain common identifying information, is termed linked microdata. Linked microdata attributes can add considerable value to organizations by enabling them to perform analysis that can provide important information on individual (or record-level) data items. We illustrate practical examples of the need and benefits of sharing linked microdata and identify important privacy issues relating to this context. Based on a conditional distribution approach, we develop a procedure (SASH) for sharing masked attributes in linked microdata that addresses these privacy issues. Our experimental results show that SASH achieves a priori expectations of analytical usefulness, without either party having to provide true values of attribute data. Our results also show that an ad hoc approach such as data swapping, cannot achieve privacy without sacrificing usefulness or vice versa. Our study should provide immediate practical benefits to organizations interested in secure attribute sharing of linked microdata. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.dss.2015.10.005 | Decision Support Systems |
Keywords | Field | DocType |
Secure attribute sharing,Microdata,Confidentiality,Privacy-preserving data sharing | Data mining,Swap (computer programming),Conditional probability distribution,Sash window,Confidentiality,Computer science,A priori and a posteriori,Robustness (computer science),Microdata (HTML) | Journal |
Volume | Issue | ISSN |
81 | C | 0167-9236 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Krishnamurty Muralidhar | 1 | 236 | 28.06 |
| Rathindra Sarathy | 2 | 493 | 35.29 |
| Han Li | 3 | 235 | 10.29 |