Managing key hierarchies for access control enforcement: Heuristic approaches

Author: Carlo Blundo, Stelvio Cimato, Sabrina De Capitani di Vimercati, Alfredo De Santis, Sara Foresti, Stefano Paraboschi, Pierangela Samarati

Source: Computers & Security, vol.29, 2010, pp. 533-547

Presenter: Tsuei-Hung Sun

Date: 2010/7/6

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Outline

ه Introduction ه Motivation ه Schemeه Advantage vs. weakness ه Conclusion

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Introduction

ه Data outsourcing promises higher availability and more effective disaster protection than in-house operations.

ه It need to protect the privacy of the data from the so called honest-but-curious servers.

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Introduction

ه Prim's algorithm

Image source: Prim's algorithm, 清華大學資訊工程所 劉炯朗 教授 http://nthucad.cs.nthu.edu.tw/~yyliu/personal/nou/04ds/prim.html

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Motivation

ه Existing approaches do not address the problem of supporting different access authorizations for different users.

ه Enforcing the authorization policy by heuristic and minimizing the number of keys to be maintained by the system and distributed to users.

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Scheme

ه Basic concept

Fig. Access matrixFig. User tree

acl(r): access control list of r, users that can access r. Ex. acl(r2) = {A, C}cap(u): capability list of u, resources that u can access. Ex. cap(C) = {r2 , r4 , r6}v.acl: set of users represented by vertex v.v.key: key associated with v.

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Scheme

ه Integer Linear Programming (ILP) minimum user tree

Fig. General minimum weight user tree Fig. ILP minimum weight user tree

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Scheme

ه ILP minimum user tree problem is formulated as follows

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Scheme

ه Three families of heuristicsه sibling-based (S)ه leaf-based (L) ه mixed (M)

ه Three preference criteriaه rnd: at random.ه max: |vi.acl| + |vj.acl| is maximum, ties are broken rando

mly.ه min: |vi.acl| + |vj.acl| is minimum, ties are broken rando

mly.

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Sibling-based heuristic

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Sibling-based heuristic

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Leaves-based heuristic

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Leaves-based heuristic

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Mixed heuristics

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Experimental result

ه Compare three heuristics with Damiani’s approach.

Fig. sibling-based heuristic with different preference criteria.

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Experimental result

ه Compare three heuristics adopting the min preference criterion with Damiani’s approach.

Fig. Percentage of times each heuristic returns a solution at distance d from the lowest weight solution computed.

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Advantage vs. weakness

ه Advantageه Three families of heuristics preference better than Dami

ani’s heuristics.ه Integer linear programming formulation of the minimiz

ation problem.

ه Weaknessه Execution time of the mixed heuristic is higher than the

time requested by the other heuristics.ه High variability of the time necessary to solve the ILP

problem.

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Conclusion

ه Protect the resource confidentiality from both unauthorized users and ‘‘honest-but-curious’’ servers.

ه Most of the existing efforts focus on the techniques for the evaluation of queries on encrypted outsourced data.

ه Integrating access control and encryption and by exploiting key derivation methods as a way for minimizing the number of keys distributed to users.

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Referencesه Prim's algorithm http://en.wikipedia.org/wiki/Prim%27s_algorithm (2010/7/7)ه 普林演算法 (Prim's algorithm) http://nthucad.cs.nthu.edu.tw/~yyliu/personal/

nou/04ds/prim.html (2010/7/8)ه Graph (mathematics) http://en.wikipedia.org/wiki/Undirected_graph (2010/7/

7)ه Minimum spanning tree http://en.wikipedia.org/wiki/Minimum_spanning_tree

(2010/7/7)ه Regular graph http://en.wikipedia.org/wiki/Regular_graph (2010/7/8)ه Graph factorization http://en.wikipedia.org/wiki/Graph_factorization (2010/7/

8)ه Directed acyclic graph http://en.wikipedia.org/wiki/Directed_acyclic_graph

(2010/7/8)ه Linear programming http://en.wikipedia.org/wiki/Linear_programming (2010/

7/9)

Thank you