Pasquale Malacaria, Professor of
School of Electronic Engineering and
Queen Mary University of London, Mile End Road, E1 4NS
+44 020 7
Research interest: Information Theory, Security,
Verification, Game Theory.
Using reliability analysis for security analysis: "Quantifying Information Leaks using Reliability Analysis" (SPIN 2014).
Using game theory for optimal allocation of security administrators time, "
Game Theory Meets Information Security Management" (IFIP SEC 2014).
A paper about a fast algorithm for quantitative information flow using abstract model counting,"Abstract model counting: a novel approach for quantification of information leaks."
(ACM ASIACCS 2014).
A short paper on using multi-objective knapsack for optimal cyber-security investment, "How to Spend it: Optimal Investment for Cyber Security"
A short video interview about the work on the Thermodynamics of confidentiality.
Is on YouTube here
(paper here ).
December 2013. Just back from Costa Rica where I participated in a very exciting workshop
on information and processes.
On the theme of videos here is an older one filmed in 2011 at the Bellairs institute
in Barbados, is a talk on Quantitative Information Flow (an algebraic introduction). Is on YouTube here.
ETAPS 2013 in
Rome. Friday the 22nd was a very special ETAPS day. In the morning we
celebrated Corrado Bohm 90th birthday. He was my master's supervisor and
I was glad to be part of this celebration. In the afternoon there was a
special session for our colleague and friend Kohei Honda who sadly
passed away last December; I gave a talk/tribute about his work on Game
ETAPS 2015 will be here at Queen Mary, me and Nikos Tzevelekos will co-chair the
Jonathan has a very nice development to
of the techniques we introduced in the ACSAC paper. Read about it here.
Paper accepted at VMCAI 2013
. The paper is about leakage analysis of Markovian processes
with application to randomized protocols.
I am also involved in the
Research Institute in Automated Program Analysis and Verification.
My involvement is as a PI in the project
on compositional security analysis for binaries
"The Thermodynamics of Confidentiality" has been accepted for the
Computer Security Foundations Symposium . The overall
contribution of this paper can be seen as laying down in a precise sense
the thermodynamic foundations of confidentiality. In a follow up paper
("Thermodynamic Aspects of Confidentiality" in Information and Computation 2013 vol. 226 ) we extend this
work to side channel analysis of Brownian Computers. We found out that
there are timing channels in Brownian Computers that don't exist in
ordinary computers. Can you guess what they are?
Recent work: quantitative
analysis of leakage of real code, e.g. quantifying leakage of
confidential information in the Linux Kernel. The paper ( ACSAC 2010 )
is here . Also current interests include game
theoretical analysis for security risk assessment.
( PLAS 2008
, ASIACCS 2009) addresses the
problem of maximal leakage of secure information, translating it into
the information theoretical problem of channel capacity. The problem is
then approached using Lagrange multipliers. We also use Lagrange
Multipliers to compute loss of anonymity in anonymity protocols ( ASIACCS 2009 ).
Recent invited talks
Departmental Seminar, Department of Computer Science, University of Oxford, March 2014
Workshop on Information and Processes CIAPA, Costa Rica
December 15 - 18, 2013
investigator in the EPSRC project EP/K032011/1 "Compositional Security Analysis for Binaries"
investigator in the EPSRC project EP/K005820/1 "Games and Abstraction: The Science of
Cyber Security" (2013-2016)
principal investigator in the EPSRC
project "Quantitative Information Flow"
investigator in the EPSRC project "Model Checking and Program Analysis
for Quantifying Interference".
the EPSRC platform grant "Extreme Reasoning".
Interested in a PhD? Funding available, further details click
here. Applicants should be able to show some knowledge and
interest in my areas of research.
interested in applications of information theory to machine learning in particular Adaboost. Adaboost significantly boosts the
performance of classifiers, which are algorithms that allow to
classify new cases based on previous cases. An example could be a
program which classify if a patient has/has not cancer given the
symptoms based on a database of previous cases (this database is called
the training set). In this paper
we related Adaboost with Kelly's theory of optimal betting
and by doing so we significantly simplify the Adaboost algorithm.
Past works: Game
Semantics and its algorithmic applications. Game semantics
provides a very fine graded analysis of computation. It has been used to
successfully answer some open questions in the semantic of programming
languages (Full abstraction for PCF, see publication sections). Here are some motivations for that work.