The lack of privacy protection for Internet users has been identified as a major problem in modern web browsers. Despite potentially high risk of identification by typing patterns, this topic has received little attention in both the research and general community. In this paper we present a simple but efficient statistical detection model for constructing users' identity from their typing patterns. Extensive experiments are conducted to justify the accuracy of our model. Using this model, online adversaries could uncover the identity of Web users even if they are using anonymizing services. Our goal is to raise awareness of this privacy risk to general Internet users and encourage countermeasures in future implementations of anonymous browsing techniques.

Lazy replication with snapshot isolation (SI) has emerged as a popular choice for distributed databases. However, lazy replication often requires execution of update transactions at one (master) site so that it is relatively easy for a total SI order to be determined for consistent installation of updates in the lazily replicated system. We propose a set of techniques that support update transaction execution over multiple partitioned sites, thereby allowing the master to scale. Our techniques determine a total SI order for update transactions over multiple master sites without requiring global coordination in the distributed system, and ensure that updates are installed in this order at all sites to provide consistent and scalable replication with SI. We present ConfluxDB, a PostgreSQL-based implementation of our techniques, and demonstrate its effectiveness through experimental evaluation.

Online social networks have become important vehicles for connecting people for work and leisure. As these networks grow, data that are stored over these networks also grow, and management of these data becomes a challenge. Graph data models are a natural fit for representing online social networks but need to support distribution to allow the associated graph databases to scale while offering acceptable performance. We provide scalability by considering methods for partitioning graph databases and implement one within the Neo4j architecture based on distributing the vertices of the graph. We evaluate its performance in several simple scenarios and demonstrate that it is possible to partition a graph database without incurring significant overhead other than that required by network delays. We identify and discuss several methods to reduce the observed network delays in our prototype.