Data mining across different companies, organizations, online shops, or the likes is necessary so as to discover valuable shared patterns, associations, trends, or dependencies in their shared data. Privacy, however, is a concern. In many situations it is required that data mining should be conducted without any privacy being violated. In response to this requirement, in this paper we propose an effective distributed privacy-preserving data mining approach called SDDM. SDDM is characterized by its ability to resist collusion. Unless the number of colluding sites in a distributed system is larger than or equal to 4, privacy cannot be violated. Results of performance study demonstrated the effectiveness of SDDM.

In recent years, more emphasis is placed on the performance of massive databases. It is often required not only that database systems provide high throughputs with rapid response times, but also that they are fully available 24-hours-per-day and 7-days-per-week. Requirements for throughput and response time can be satisfied by upgrading the hardware. As a result, databases in the old hardware environment have to be moved to the new one. Moving a database, however, generally requires taking the database off line for a long time, which is unacceptable for numerous applications. In this paper, a very practical and important subject is addressed: how to upgrade the hardware on line, i.e., how to move a database from an old hardware environment to a new one concurrently with users' reading and writing of the database. A technique for this purpose is proposed. We have implemented a prototype based on this technique. Our experiments with the prototype shown that compared with conventional off-line approach, the proposed technique could give a performance improvement by more than 85% in the query-bound environment and 40% in the update-bound environment.

Parallel Transaction Processing (TP) systems have great potential to serve the ever-increasing demands for high transaction processing rate. This potential, however, may not be reached due to the data contention and the widely-used two-phase locking (2PL) Concurrency Control (CC) method. In this paper, a distributed locking-based CC policy called LWDC (Local Wait-Depth Control) was proposed for dealing with this problem for the shared-nothing parallel TP system. On the basis of the LWDC policy, an algorithm called LWDCk was designed. Using simulation LWDCk was compared with the 2PL and the base-line Distributed Wait-Depth Limited (DWDL) CC methods. Simulation studies show that the new algorithm offers better system performance than those compared.