scholarly journals Combinatorial Algorithms and Methods for Security of Statistical Databases Related to the Work of Mirka Miller

2018 ◽  
pp. 383-394
Author(s):  
Andrei Kelarev ◽  
Jennifer Seberry ◽  
Leanne Rylands ◽  
Xun Yi
Keyword(s):  
Combinatorial Algorithms ◽  
Statistical Databases

Query evaluability in statistical databases

1990 ◽  
Vol 2 (4) ◽  
pp. 425-430 ◽  
Author(s):  
F.M. Malvestuto ◽  
M. Moscarini
Keyword(s):  
Statistical Databases

scholarly journals Combinatorial algorithms for structural variation detection in high-throughput sequenced genomes

2009 ◽  
Vol 19 (7) ◽  
pp. 1270-1278 ◽  
Author(s):  
F. Hormozdiari ◽  
C. Alkan ◽  
E. E. Eichler ◽  
S. C. Sahinalp
Keyword(s):  
High Throughput ◽  
Structural Variation ◽  
Combinatorial Algorithms

Security of statistical databases

1981 ◽  
Vol 6 (1) ◽  
pp. 95-112 ◽  
Author(s):  
Jan Schlörer
Keyword(s):  
Statistical Databases

Security of statistical databases

1980 ◽  
Vol 5 (4) ◽  
pp. 467-492 ◽  
Author(s):  
Jan Schlöer
Keyword(s):  
Statistical Databases

Combined Graph Layout Algorithms for Automated Sketching of Kinematic Chains

2012 ◽  
Author(s):  
Martín A. Pucheta ◽  
Nicolás E. Ulrich ◽  
Alberto Cardona
Keyword(s):  
Computational Cost ◽  
Kinematic Chain ◽  
Initial Position ◽  
Graph Representation ◽  
Combinatorial Algorithms ◽  
Graph Layout ◽  
Kinematic Chains ◽  
Aesthetic Characteristics ◽  
Edge Crossings ◽  
Independent Loops

The graph layout problem arises frequently in the conceptual stage of mechanism design, specially in the enumeration process where a large number of topological solutions must be analyzed. Two main objectives of graph layout are the avoidance or minimization of edge crossings and the aesthetics. Edge crossings cannot be always avoided by force-directed algorithms since they reach a minimum of the energy in dependence with the initial position of the vertices, often randomly generated. Combinatorial algorithms based on the properties of the graph representation of the kinematic chain can be used to find an adequate initial position of the vertices with minimal edge crossings. To select an initial layout, the minimal independent loops of the graph can be drawn as circles followed by arcs, in all forms. The computational cost of this algorithm grows as factorial with the number of independent loops. This paper presents a combination of two algorithms: a combinatorial algorithm followed by a force-directed algorithm based on spring repulsion and electrical attraction, including a new concept of vertex-to-edge repulsion to improve aesthetics and minimize crossings. Atlases of graphs of complex kinematic chains are used to validate the results. The layouts obtained have good quality in terms of minimization of edge crossings and maximization of aesthetic characteristics.

scholarly journals Encrypted Databases for Differential Privacy

2019 ◽  
Vol 2019 (3) ◽  
pp. 170-190
Author(s):  
Archita Agarwal ◽  
Maurice Herlihy ◽  
Seny Kamara ◽  
Tarik Moataz
Keyword(s):  
Statistical Analysis ◽  
Real World ◽  
Building Block ◽  
Differential Privacy ◽  
Statistical Databases ◽  
The Past ◽  
Statistical Queries ◽  
Encrypted Database

Abstract The problem of privatizing statistical databases is a well-studied topic that has culminated with the notion of differential privacy. The complementary problem of securing these differentially private databases, however, has—as far as we know—not been considered in the past. While the security of private databases is in theory orthogonal to the problem of private statistical analysis (e.g., in the central model of differential privacy the curator is trusted) the recent real-world deployments of differentially-private systems suggest that it will become a problem of increasing importance. In this work, we consider the problem of designing encrypted databases (EDB) that support differentially-private statistical queries. More precisely, these EDBs should support a set of encrypted operations with which a curator can securely query and manage its data, and a set of private operations with which an analyst can privately analyze the data. Using such an EDB, a curator can securely outsource its database to an untrusted server (e.g., on-premise or in the cloud) while still allowing an analyst to privately query it. We show how to design an EDB that supports private histogram queries. As a building block, we introduce a differentially-private encrypted counter based on the binary mechanism of Chan et al. (ICALP, 2010). We then carefully combine multiple instances of this counter with a standard encrypted database scheme to support differentially-private histogram queries.

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