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International Conference on Cryptology in India

INDOCRYPT 2011: Progress in Cryptology – INDOCRYPT 2011 pp 3–21Cite as

PKDPA: An Enhanced Probabilistic Differential Power Attack Methodology

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 7107))

Abstract

The paper presents an enhancement of univariate Differential Power Analysis (DPA), referred to as Probable Key Differential Power Analysis (PKDPA). The proposed analysis uses the standard Difference of Means (DoM) test as the distinguisher and employs its enhancement strategy to reduce the number of power traces required to mount the attack. Theoretical analysis for the developed attack has been furnished to justify the efficiency of the proposed attack in retrieving the key using significantly less number of traces compared to conventional DPA attacks. The theoretical claims have been supported by extensive experiments on real life attacks mounted on Field Programmable Gate Array (FPGA) implementations of the Data Encryption Standard (DES), Triple-DES (3-DES) and the Advanced Encryption Standard (AES). The efficacy of the proposed method is further proved by attacking a masked implementation of AES using only 13,000 power traces.

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Authors
  1. Dhiman Saha
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  2. Debdeep Mukhopadhyay
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  3. Dipanwita RoyChowdhury
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Illinois at Chicago, 60607–7053, Chicago, IL, USA

    Daniel J. Bernstein

  2. Indian Institute of Science, India

    Sanjit Chatterjee

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Saha, D., Mukhopadhyay, D., RoyChowdhury, D. (2011). PKDPA: An Enhanced Probabilistic Differential Power Attack Methodology. In: Bernstein, D.J., Chatterjee, S. (eds) Progress in Cryptology – INDOCRYPT 2011. INDOCRYPT 2011. Lecture Notes in Computer Science, vol 7107. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25578-6_3

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  • DOI: https://doi.org/10.1007/978-3-642-25578-6_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25577-9

  • Online ISBN: 978-3-642-25578-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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