Ananda Swarup Das

Privacy Preserving Cooperative Clustering Service

In the field of access control, delegation is an important aspect that is considered as a part of the administration mechanism. Thus, a complete access control must provide a flexible administration model to manage delegation. Unfortunately, to our best knowledge, there is no complete model for describing all delegation requirements for role-based access control. Therefore, proposed models are often extended to consider new delegation characteristics, which is a complex task to manage and necessitate the redefinition of these models. In this paper we describe a new delegation approach for extended role-based access control models. We show that our approach is flexible and is sufficient to manage all delegation requirements.The growth of Internet has opened up new avenues for business and corporate model. Information sharing over Internet can help business houses in better cooperative strategic planning and growth. However despite such an impact, business houses are quite reluctant to share information because of the fear of information leakage. In this paper we study and propose an elegant, simple and practical solution for the problem of how can one party avail the data clustering service of another party without affecting each others privacy. In our solution, we introduce the following two problems: (a) Secure multiparty computation of a depth of a query point, (b) Secure multiparty computation of whether a query point is a hull vertex. To the best of our knowledge this is the first time in literature that the aforementioned problems have been considered in privacy preserving framework.

Range aggregate maximal points in the plane

In this work, we study the problem of reporting and counting maximal points in a query rectangle for a set of n integer points that lie on an n×n grid. A point is said to be maximal inside a query rectangle if it is not dominated by any other point inside the query rectangle. Our model of computation is unit-cost RAM model with word size of O(logn) bits. For the reporting version of the problem, we present a data structure of size

On Counting Range Maxima Points in Plane

O(n\frac{\log n}{\log\log n})

Predictive Caching Framework for Mobile Wireless Networks

words and support querying in

Counting Maximal Points in a Query Orthogonal Rectangle

O(\frac{\log n}{\log\log n}+k)

Privacy Preserving Computation of Shortest Path in Presence of a Single Convex Polygonal Obstacle

time where k is the size of the output. For the counting version, we present a data structure of size

AnnoFin–A hybrid algorithm to annotate financial text

O(n\frac{\log^{2} n}{\log\log n})

Linear space adaptive data structures for planar range reporting

words which supports querying in

Reporting and counting maximal points in a query orthogonal rectangle

O(\frac{\log^{\frac{3}{2}}n} {\log\log n})

On Finding Skyline Points for Range Queries in Plane

. Both the data structures are static in nature. The reporting version of the problem has been studied in [1] and [5]. To the best of our knowledge, this is the first sub-logarithmic result for the reporting version and the first work for the counting version of the problem.