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Mitigation Techniques for Attacks on 1-Dimensional Databases that Support Range Queries

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Book cover Information Security (ISC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11723))

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Abstract

In recent years, a number of attacks have been developed that can reconstruct encrypted one-dimensional databases that support range queries under the persistent passive adversary model. These attacks allow an (honest but curious) adversary (such as the cloud provider) to find the order of the elements in the database and, in some cases, to even reconstruct the database itself.

In this paper we present two mitigation techniques to make it harder for the adversary to reconstruct the database. The first technique makes it impossible for an adversary to reconstruct the values stored in the database with an error smaller than k, for k chosen by the client. By fine-tuning k, the user can increase the adversary’s error at will.

The second technique is targeted towards adversaries who have managed to learn the distribution of the queries issued. Such adversaries may be able to reconstruct most of the database after seeing a very small (i.e. poly-logarithmic) number of queries. To neutralize such adversaries, our technique turns the database to a circular buffer. All known techniques that exploit knowledge of distribution fail, and no technique can determine which record is first (or last) based on access pattern leakage.

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Notes

  1. 1.

    Depending on the distribution, WQ may need to issue several queries. For the purposes of discussion, at this point we assume that just one extra query \([a',b']\) is issued.

  2. 2.

    Actually, the same should be true for all combinations of values \(v_i\) and \(v_j\) as we will later show.

  3. 3.

    If we issued queries one by one, the last query issued would always be a normal query.

  4. 4.

    The reader might wonder that since there are five values (1 to 5), then each value should have probability 1/5 (not 3/5) to appear in the query results. We should note however that these queries are range queries that return more than one value.

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Acknowledgments

We are grateful to Arkady Yerukhimovich for valuable comments and suggestions.

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Authors and Affiliations

  1. Brown University, Providence, RI, 02912, USA

    Evangelia Anna Markatou & Roberto Tamassia

Authors
  1. Evangelia Anna Markatou
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  2. Roberto Tamassia
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Correspondence to Evangelia Anna Markatou .

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Editors and Affiliations

  1. The Ohio State University, Columbus, OH, USA

    Zhiqiang Lin

  2. University of Maryland, College Park, MD, USA

    Charalampos Papamanthou

  3. Stony Brook University, Stony Brook, NY, USA

    Michalis Polychronakis

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Markatou, E.A., Tamassia, R. (2019). Mitigation Techniques for Attacks on 1-Dimensional Databases that Support Range Queries. In: Lin, Z., Papamanthou, C., Polychronakis, M. (eds) Information Security. ISC 2019. Lecture Notes in Computer Science(), vol 11723. Springer, Cham. https://doi.org/10.1007/978-3-030-30215-3_12

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  • DOI: https://doi.org/10.1007/978-3-030-30215-3_12

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  • Print ISBN: 978-3-030-30214-6

  • Online ISBN: 978-3-030-30215-3

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