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Quantification of Digital Forensic Hypotheses Using Probability Theory Richard E Overill & Jantje A M Silomon King’s College London Kam-Pui Chow & Hayson Tse University of Hong Kong

Quantification of Digital Forensic Hypotheses Using Probability Theory Richard E Overill & Jantje A M Silomon King’s College London Kam-Pui Chow & Hayson

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Page 1: Quantification of Digital Forensic Hypotheses Using Probability Theory Richard E Overill & Jantje A M Silomon King’s College London Kam-Pui Chow & Hayson

Quantification of Digital Forensic Hypotheses UsingProbability Theory

Richard E Overill & Jantje A M SilomonKing’s College London

Kam-Pui Chow & Hayson TseUniversity of Hong Kong

Page 2: Quantification of Digital Forensic Hypotheses Using Probability Theory Richard E Overill & Jantje A M Silomon King’s College London Kam-Pui Chow & Hayson

Synopsis

• Introduction & Background• Probabilistic Models• Simplifying Assumptions• Results & Interpretation• Summary & Conclusions• Questions & Comments?

Page 3: Quantification of Digital Forensic Hypotheses Using Probability Theory Richard E Overill & Jantje A M Silomon King’s College London Kam-Pui Chow & Hayson

Introduction & Background

• Possession of Child Pornography (CP) is a serious offence in HK, UK and elsewhere

• Under prosecution, 2 common defences are:– Trojan Horse (when many CP images are recovered)– Inadvertent (when a few CP images are recovered

amongst many non-CP images)• We used complexity theory to quantify the

plausibility of the THD (ICDFI-2012, ICDFI-2013)• Here we use probability theory to quantify the

plausibility of the Inadvertent Defence (ID)

Page 4: Quantification of Digital Forensic Hypotheses Using Probability Theory Richard E Overill & Jantje A M Silomon King’s College London Kam-Pui Chow & Hayson

Probabilistic Models

• Greedy download – every image on website– the probability distribution is trivially singular.

• Selective download – a representative sample of images on website– Infinite website: probabilities do not change as

download proceeds – use the Binomial Theorem;– Finite website: probabilities change as images are

downloaded – use the “Urn/Bag of balls” model.

Page 5: Quantification of Digital Forensic Hypotheses Using Probability Theory Richard E Overill & Jantje A M Silomon King’s College London Kam-Pui Chow & Hayson

Simplifying Assumptions

• Random browsing behaviour.• Random distribution of CP images on website.• No duplicates in download. • Single download session.• Single website.• Single computer.• One individual.

Page 6: Quantification of Digital Forensic Hypotheses Using Probability Theory Richard E Overill & Jantje A M Silomon King’s College London Kam-Pui Chow & Hayson

Results & Interpretation

• 2 actual HK cases:– Case 1: 248/30,000 images were CP (2010);– Case 2: 84/714,430 images were of CP (2013).

• “worst case” (prosecution) results:

“worst-case” probabilities Finite Model Infinite Model

Case 1 0.0304 0.0254

Case 2 0.0807 0.0435

Page 7: Quantification of Digital Forensic Hypotheses Using Probability Theory Richard E Overill & Jantje A M Silomon King’s College London Kam-Pui Chow & Hayson

Case 1 - Probability Distributions

Finite Model Infinite Model

Page 8: Quantification of Digital Forensic Hypotheses Using Probability Theory Richard E Overill & Jantje A M Silomon King’s College London Kam-Pui Chow & Hayson

Case 2 - Probability Distributions

Finite Model Infinite Model

Page 9: Quantification of Digital Forensic Hypotheses Using Probability Theory Richard E Overill & Jantje A M Silomon King’s College London Kam-Pui Chow & Hayson

Summary & Conclusions• Infinite model worst-case results (2.5% & 4.3%)

suggest a criminal prosecution is feasible.• Finite model worst-case results (3% & 8%) also

suggest a criminal prosecution is feasible but are influenced by assumptions of website size.

• Non-worst-case probabilities fall off rapidly:σ ≈ √μ

• Simple probability models can be used to quantify the plausibility of the Inadvertent defence (ID) against possession of CP.