C4.5: programs for machine learning
C4.5: programs for machine learning
Persona: an online social network with user-defined privacy
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
Trust no one: a decentralized matching service for privacy in location based services
Proceedings of the second ACM SIGCOMM workshop on Networking, systems, and applications on mobile handhelds
Privad: practical privacy in online advertising
Proceedings of the 8th USENIX conference on Networked systems design and implementation
RePriv: Re-imagining Content Personalization and In-browser Privacy
SP '11 Proceedings of the 2011 IEEE Symposium on Security and Privacy
Towards statistical queries over distributed private user data
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
Koi: a location-privacy platform for smartphone apps
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
Your browsing behavior for a big mac: economics of personal information online
Proceedings of the 22nd international conference on World Wide Web
| Hi-index | 0.00 |
Data breaches, e.g. malware, network intrusions, or physical theft, that lead to the compromise of users' personal data, happen often. The impacted companies lose reputation and have to spend millions of dollars providing affected users with identity and credit monitoring services. Users can suffer from fraudulent transactions and identity theft. At present, there are no mechanisms that both cover the risk from accidental data breaches and incentivise best practices that would prevent such breaches. This paper proposes a data breach insurance mechanism and the associated risk assessment technology to meet these goals. In so doing, we break from (failed) past approaches that seek to solve the problem solely through technology.