Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
Executing SQL over encrypted data in the database-service-provider model
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Efficient execution of joins in a star schema
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
PicoDBMS: Scaling down database techniques for the smartcard
The VLDB Journal — The International Journal on Very Large Data Bases
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
GhostDB: querying visible and hidden data without leaks
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
GnatDb: a small-footprint, secure database system
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Hi-index | 0.01 |
Imagine that you have been entrusted with private data, such as corporate product information, sensitive government information, or symptom and treatment information about hospital patients. You may want to issue queries whose result will combine private and public data, but private data must not be revealed, say, to the prying eyes of some insurance fraudster. GhostDB is an architecture and system to achieve this. You carry private data in a smart USB device (a large Flash persistent store combined with a tamper and snoop-resistant CPU and small RAM). When the key is plugged in, you can issue queries that link private and public data and be sure that the only information revealed to a potential spy is which queries you pose and the public data you access. Queries linking public and private data entail novel distributed processing techniques on extremely unequal devices (standard computer and smart USB device) in which data flows in only one direction: from public to private. This demonstration shows GhostDB's query processing in action.