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Secrecy-Preserving Reasoning and Query Answering in Probabilistic Description Logic Knowledge Bases

14 pagesPublished: October 19, 2017


In this paper we study Secrecy-Preserving Query Answering problem under
the OpenWorld Assumption (OWA) for Prob-EL>0;=1 Knowledge Bases
(KBs). We have designed a tableau procedure to compute a semi model M
over the given KB which eventually is equivalent to a probabilistic model
to KB. Given a secrecy set S, which is a finite set of assertions, we
compute a function E, called an envelope of S, which assigns a set E() of
assertions to each world in the semi modal M. E provides logical protection to the secrecy set S against the reasoning of a querying agent. Once the semi model M and an envelope E are computed, we define the secrecy-preserving semi model ME.
Based on the information available in ME, assertional queries with probabilistic
operators can be answered eciently while preserving secrecy. To
the best of our knowledge, this work is first one studying secrecy-preserving
reasoning in description logic augmented with probabilistic operators. When
the querying agent asks a query q, the reasoner answers “Yes” if information
about q is available in ME; otherwise, the reasoner answers “Unknown”. Being
able to answer “Unknown” plays a key role in protecting secrecy under
OWA. Since we are not computing all the consequences of the knowledge
base, answers to the queries based on just secrecy-preserving semi model
ME could be erroneous. To fix this problem, we further augment our algorithms
by providing recursive query decomposition algorithm to make the
query answering procedure foolproof.

Keyphrases: knowledge bases, Probabilistic Description Logic, query answering, Secrecy-Preserving Reasoning

In: Christoph Benzmüller, Christine Lisetti and Martin Theobald (editors). GCAI 2017. 3rd Global Conference on Artificial Intelligence, vol 50, pages 188--201

BibTeX entry
  author    = {Gopalakrishnan Krishnasamy Sivaprakasam and Adrienne Raglin and Douglas Summers-Stay and Giora Slutzki},
  title     = {Secrecy-Preserving Reasoning and Query Answering in Probabilistic Description Logic Knowledge Bases},
  booktitle = {GCAI 2017. 3rd Global Conference on Artificial Intelligence},
  editor    = {Christoph Benzm\textbackslash{}"uller and Christine Lisetti and Martin Theobald},
  series    = {EPiC Series in Computing},
  volume    = {50},
  pages     = {188--201},
  year      = {2017},
  publisher = {EasyChair},
  bibsource = {EasyChair,},
  issn      = {2398-7340},
  url       = {},
  doi       = {10.29007/npd4}}
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