19,171 research outputs found
SoK: Cryptographically Protected Database Search
Protected database search systems cryptographically isolate the roles of
reading from, writing to, and administering the database. This separation
limits unnecessary administrator access and protects data in the case of system
breaches. Since protected search was introduced in 2000, the area has grown
rapidly; systems are offered by academia, start-ups, and established companies.
However, there is no best protected search system or set of techniques.
Design of such systems is a balancing act between security, functionality,
performance, and usability. This challenge is made more difficult by ongoing
database specialization, as some users will want the functionality of SQL,
NoSQL, or NewSQL databases. This database evolution will continue, and the
protected search community should be able to quickly provide functionality
consistent with newly invented databases.
At the same time, the community must accurately and clearly characterize the
tradeoffs between different approaches. To address these challenges, we provide
the following contributions:
1) An identification of the important primitive operations across database
paradigms. We find there are a small number of base operations that can be used
and combined to support a large number of database paradigms.
2) An evaluation of the current state of protected search systems in
implementing these base operations. This evaluation describes the main
approaches and tradeoffs for each base operation. Furthermore, it puts
protected search in the context of unprotected search, identifying key gaps in
functionality.
3) An analysis of attacks against protected search for different base
queries.
4) A roadmap and tools for transforming a protected search system into a
protected database, including an open-source performance evaluation platform
and initial user opinions of protected search.Comment: 20 pages, to appear to IEEE Security and Privac
Maladaptive bias for extrahippocampal navigation strategies in aging humans.
Efficient spatial navigation requires not only accurate spatial knowledge but also the selection of appropriate strategies. Using a novel paradigm that allowed us to distinguish between beacon, associative cue, and place strategies, we investigated the effects of cognitive aging on the selection and adoption of navigation strategies in humans. Participants were required to rejoin a previously learned route encountered from an unfamiliar direction. Successful performance required the use of an allocentric place strategy, which was increasingly observed in young participants over six experimental sessions. In contrast, older participants, who were able to recall the route when approaching intersections from the same direction as during encoding, failed to use the correct place strategy when approaching intersections from novel directions. Instead, they continuously used a beacon strategy and showed no evidence of changing their behavior across the six sessions. Given that this bias was already apparent in the first experimental session, the inability to adopt the correct place strategy is not related to an inability to switch from a firmly established response strategy to an allocentric place strategy. Rather, and in line with previous research, age-related deficits in allocentric processing result in shifts in preferred navigation strategies and an overall bias for response strategies. The specific preference for a beacon strategy is discussed in the context of a possible dissociation between beacon-based and associative-cue-based response learning in the striatum, with the latter being more sensitive to age-related changes
Integrating Symbolic and Neural Processing in a Self-Organizing Architechture for Pattern Recognition and Prediction
British Petroleum (89A-1204); Defense Advanced Research Projects Agency (N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (F49620-92-J-0225
An algorithmic and architectural study on Montgomery exponentiation in RNS
The modular exponentiation on large numbers is computationally intensive. An effective way for performing this operation consists in using Montgomery exponentiation in the Residue Number System (RNS). This paper presents an algorithmic and architectural study of such exponentiation approach. From the algorithmic point of view, new and state-of-the-art opportunities that come from the reorganization of operations and precomputations are considered. From the architectural perspective, the design opportunities offered by well-known computer arithmetic techniques are studied, with the aim of developing an efficient arithmetic cell architecture. Furthermore, since the use of efficient RNS bases with a low Hamming weight are being considered with ever more interest, four additional cell architectures specifically tailored to these bases are developed and the tradeoff between benefits and drawbacks is carefully explored. An overall comparison among all the considered algorithmic approaches and cell architectures is presented, with the aim of providing the reader with an extensive overview of the Montgomery exponentiation opportunities in RNS
Neural Distributed Autoassociative Memories: A Survey
Introduction. Neural network models of autoassociative, distributed memory
allow storage and retrieval of many items (vectors) where the number of stored
items can exceed the vector dimension (the number of neurons in the network).
This opens the possibility of a sublinear time search (in the number of stored
items) for approximate nearest neighbors among vectors of high dimension. The
purpose of this paper is to review models of autoassociative, distributed
memory that can be naturally implemented by neural networks (mainly with local
learning rules and iterative dynamics based on information locally available to
neurons). Scope. The survey is focused mainly on the networks of Hopfield,
Willshaw and Potts, that have connections between pairs of neurons and operate
on sparse binary vectors. We discuss not only autoassociative memory, but also
the generalization properties of these networks. We also consider neural
networks with higher-order connections and networks with a bipartite graph
structure for non-binary data with linear constraints. Conclusions. In
conclusion we discuss the relations to similarity search, advantages and
drawbacks of these techniques, and topics for further research. An interesting
and still not completely resolved question is whether neural autoassociative
memories can search for approximate nearest neighbors faster than other index
structures for similarity search, in particular for the case of very high
dimensional vectors.Comment: 31 page
A Cognitive Mind-map Framework to Foster Trust
The explorative mind-map is a dynamic framework, that emerges automatically
from the input, it gets. It is unlike a verificative modeling system where
existing (human) thoughts are placed and connected together. In this regard,
explorative mind-maps change their size continuously, being adaptive with
connectionist cells inside; mind-maps process data input incrementally and
offer lots of possibilities to interact with the user through an appropriate
communication interface. With respect to a cognitive motivated situation like a
conversation between partners, mind-maps become interesting as they are able to
process stimulating signals whenever they occur. If these signals are close to
an own understanding of the world, then the conversational partner becomes
automatically more trustful than if the signals do not or less match the own
knowledge scheme. In this (position) paper, we therefore motivate explorative
mind-maps as a cognitive engine and propose these as a decision support engine
to foster trust.Comment: 5 pages, 4 Figures, Extended Version, presented at the 5th
International Conference on Natural Computation, 200
Using correlation matrix memories for inferencing in expert systems
Outline of The Chapter… Section 16.2 describes CMM and the Dynamic Variable Binding Problem. Section 16.3 deals with how CMM is used as part of an inferencing engine. Section 16.4 details the important performance characteristics of CMM
- …