We present history-independent alternatives to a B-tree, the primary indexing data structure used in databases. A data structure is history independent (HI) if it is impossible to deduce any information by examining the bit representation of the data structure that is not already available through the API. We show how to build a history-independent cache-oblivious B-tree and a history-independent external-memory skip list. One of the main contributions is a data structure we build on the way - a history-independent packed-memory array (PMA). The PMA supports efficient range queries, one of the most important operations for answering database queries. Our HI PMA matches the asymptotic bounds of prior non-HI packed-memory arrays and sparse tables. Specifically, a PMA maintains a dynamic set of elements in sorted order in a linearsized array. Inserts and deletes take an amortized O(log2 N) element moves with high probability. Simple experiments with our implementation of HI PMAs corroborate our theoretical analysis. Comparisons to regular PMAs give preliminary indications that the practical cost of adding history-independence is not too large. Our HI cache-oblivious B-tree bounds match those of prior non-HI cache-oblivious B-trees. Searches take O(logB N) I/Os; inserts and deletes take O(log2N/B + logB N) amortized I/Os with high probability; and range queries returning k elements take O(logB N + k/B) I/Os. Our HI external-memory skip list achieves optimal bounds with high probability, analogous to in-memory skip lists: O(logB N) I/Os for point queries and amortized O(logB N) I/Os for inserts/deletes. Range queries returning k elements run in O(logB N + k/B) I/Os. In contrast, the best possible high-probability bounds for inserting into the folklore B-skip list, which promotes elements with probability 1/B, is just Θ(log N) I/Os. This is no better than the bounds one gets from running an inmemory skip list in external memory.
Data security and availability for operational use are frequently seen as conflicting goals. Research on searchable encryption and homomorphic encryption are a start, but they typically build from encryption methods that, at best, provide protections based on problems assumed to be computationally hard. By contrast, data encoding methods such as secret sharing provide information-theoretic data protections. Archives that distribute data using secret sharing can provide data protections that are resilient to malicious insiders, compromised systems, and untrusted components. In this paper, we create the Serial Interpolation Filter, a method for storing and interacting with sets of data that are secured and distributed using secret sharing. We provide the ability to operate over set-oriented data distributed across multiple repositories without exposing the original data. Furthermore, we demonstrate the security of our method under various attacker models and provide protocol extensions to handle colluding attackers. The Serial Interpolation Filter provides information-theoretic protections from a single attacker and computationally hard protections from colluding attackers.
This report documents our first year efforts to address the use of many-core processors for high performance cyber protection. As the demands grow for higher bandwidth (beyond 1 Gbits/sec) on network connections, the need to provide faster and more efficient solution to cyber security grows. Fortunately, in recent years, the development of many-core network processors have seen increased interest. Prior working experiences with many-core processors have led us to investigate its effectiveness for cyber protection tools, with particular emphasis on high performance firewalls. Although advanced algorithms for smarter cyber protection of high-speed network traffic are being developed, these advanced analysis techniques require significantly more computational capabilities than static techniques. Moreover, many locations where cyber protections are deployed have limited power, space and cooling resources. This makes the use of traditionally large computing systems impractical for the front-end systems that process large network streams; hence, the drive for this study which could potentially yield a highly reconfigurable and rapidly scalable solution.