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Validating digital forensic evidence
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This dissertation focuses on the forensic validation of computer evidence. It is a
burgeoning field, by necessity, and there have been significant advances in the detection and gathering of evidence related to electronic crimes. What makes the computer
forensics field similar to other forensic fields is that considerable emphasis is placed on the validity of the digital evidence. It is not just the methods used to collect the evidence that is a concern. What is also a problem is that perpetrators of digital crimes may be engaged in what is called anti-forensics. Digital forensic evidence techniques are deliberately thwarted and corrupted by those under investigation. In traditional forensics
the link between evidence and perpetrator's actions is often straightforward: a fingerprint on an object indicates that someone has touched the object. Anti-forensic activity would be the equivalent of having the ability to change the nature of the fingerprint before, or during the investigation, thus making the forensic evidence collected invalid or less
reliable. This thesis reviews the existing security models and digital forensics, paying
particular attention to anti-forensic activity that affects the validity of data collected in the form of digital evidence. This thesis will build on the current models in this field and suggest a tentative first step model to manage and detect possibility of anti-forensic activity. The model is concerned with stopping anti-forensic activity, and thus is not a forensic model in the normal sense, it is what will be called a âmeta-forensicâ model. A
meta-forensic approach is an approach intended to stop attempts to invalidate digital forensic evidence. This thesis proposes a formal procedure and guides forensic examiners to look at evidence in a meta-forensic way
On Approximating the Sum-Rate for Multiple-Unicasts
We study upper bounds on the sum-rate of multiple-unicasts. We approximate
the Generalized Network Sharing Bound (GNS cut) of the multiple-unicasts
network coding problem with independent sources. Our approximation
algorithm runs in polynomial time and yields an upper bound on the joint source
entropy rate, which is within an factor from the GNS cut. It
further yields a vector-linear network code that achieves joint source entropy
rate within an factor from the GNS cut, but \emph{not} with
independent sources: the code induces a correlation pattern among the sources.
Our second contribution is establishing a separation result for vector-linear
network codes: for any given field there exist networks for which
the optimum sum-rate supported by vector-linear codes over for
independent sources can be multiplicatively separated by a factor of
, for any constant , from the optimum joint entropy
rate supported by a code that allows correlation between sources. Finally, we
establish a similar separation result for the asymmetric optimum vector-linear
sum-rates achieved over two distinct fields and
for independent sources, revealing that the choice of field
can heavily impact the performance of a linear network code.Comment: 10 pages; Shorter version appeared at ISIT (International Symposium
on Information Theory) 2015; some typos correcte
Local Graph Coloring and Index Coding
We present a novel upper bound for the optimal index coding rate. Our bound
uses a graph theoretic quantity called the local chromatic number. We show how
a good local coloring can be used to create a good index code. The local
coloring is used as an alignment guide to assign index coding vectors from a
general position MDS code. We further show that a natural LP relaxation yields
an even stronger index code. Our bounds provably outperform the state of the
art on index coding but at most by a constant factor.Comment: 14 Pages, 3 Figures; A conference version submitted to ISIT 2013;
typos correcte
Graph Theory versus Minimum Rank for Index Coding
We obtain novel index coding schemes and show that they provably outperform
all previously known graph theoretic bounds proposed so far. Further, we
establish a rather strong negative result: all known graph theoretic bounds are
within a logarithmic factor from the chromatic number. This is in striking
contrast to minrank since prior work has shown that it can outperform the
chromatic number by a polynomial factor in some cases. The conclusion is that
all known graph theoretic bounds are not much stronger than the chromatic
number.Comment: 8 pages, 2 figures. Submitted to ISIT 201
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