We consider the problem of horizontally partitioning a dynamic relation across a large number of disks/nodes by the use of range partitioning. Such partitioning is often desirable in large-scale parallel databases, as well as in peer-to-peer (P2P) systems. As tuples are inserted and deleted, the partitions may need to be adjusted, and data moved, in order to achieve storage balance across the participant disks/nodes. We propose ef£cient, asymptotically optimal algorithms that ensure storage balance at all times, even against an adversarial insertion and deletion of tuples. We combine the above algorithms with distributed routing structures to architect a P2P system that supports ef£cient range queries, while simultaneously guaranteeing storage balance.

P3P [23, 24] is a set of standards that allow corporations to declare their privacy policies. Hippocratic Databases [6] have been proposed to implement such policies within a corporation's datastore. From an end-user individual's point of view, both of these rest on an uncomfortable philosophy of trusting corporations to protect his/her privacy. Recent history chronicles several episodes when such trust has been willingly or accidentally violated by corporations facing bankruptcy courts, civil subpoenas or lucrative mergers. We contend that data management solutions for information privacy must restore controls in the individual's hands. We suggest that enabling such control will require a radical rethink on modeling, release, and management of personal data.