Dynamic inference control

  • Authors:
  • Jessica Staddon

  • Affiliations:
  • Palo Alto Research Center, Palo Alto, CA

  • Venue:
  • DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
  • Year:
  • 2003

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Abstract

An inference problem exists in a multilevel database if knowledge of some objects in the database allows information with a higher security level to be inferred. Many such inferences may be prevented prior to any query processing by raising the security level of some of the objects, however this inevitably impedes information access, as a user with low authorization who queries just one of the objects with raised security must seek clearance even when not in danger of making the inference. More flexible access control is possible when inferences are prevented during query processing, however this practice can result in slow query response times. We demonstrate that access control can be made sufficiently dynamic to ensure easy access to the information users are entitled to, while retaining fast query processing. Our inference control schemes provide collusion resistance and have a query processing time that depends only on the length of the inference channels (not on the length of user query histories). In addition, our schemes provide a property we call crowd control that goes beyond collusion resistance to ensure that if a large number of users have queried all but one of the objects in an inference channel, then no one will be able to query the remaining object regardless of the level of collusion resistance provided by the scheme.