Name
Abstract | ||
|---|---|---|
We consider private information a commodity, of value to both the information holder and the information seeker. Hence, a customer can be enticed to trade his/her private information with a business in exchange for compensation. In this article, we propose to apply utility theory to allow each participant to express the value they place on each private datum and, separately, on combinations of data. The PrivacyPact protocol transmits messages that comprise possible exchanges. Each participant is prevented from making offers that necessarily have lower utility for the other partner than previous ones. The protocol is complete in that if an exchange exists that is acceptable to both, it will be found as long as neither partner exits the negotiation early. While the space of possible offers grows exponentially on the number of negotiable items, experimentation with simple strategies indicates that negotiations can converge relatively quickly. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1111/j.0824-7935.2004.00259.x | COMPUTATIONAL INTELLIGENCE |
Keywords | Field | DocType |
privacy, multi-attribute utility, negotiating agents, P3P | Data mining,Geodetic datum,Information retrieval,Multi-attribute utility,Commodity,Computer science,Artificial intelligence,Private information retrieval,Utility theory,Machine learning,Negotiation | Journal |
Volume | Issue | ISSN |
20 | 4 | 0824-7935 |
Citations | PageRank | References |
11 | 1.05 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Scott Buffett | 1 | 93 | 13.68 |
| Keping Jia | 2 | 11 | 1.39 |
| Sandy Liu | 3 | 54 | 8.47 |
| Bruce Spencer | 4 | 128 | 14.25 |
| Fang Wang | 5 | 106 | 14.59 |