9,432 research outputs found
Keeping Authorities "Honest or Bust" with Decentralized Witness Cosigning
The secret keys of critical network authorities - such as time, name,
certificate, and software update services - represent high-value targets for
hackers, criminals, and spy agencies wishing to use these keys secretly to
compromise other hosts. To protect authorities and their clients proactively
from undetected exploits and misuse, we introduce CoSi, a scalable witness
cosigning protocol ensuring that every authoritative statement is validated and
publicly logged by a diverse group of witnesses before any client will accept
it. A statement S collectively signed by W witnesses assures clients that S has
been seen, and not immediately found erroneous, by those W observers. Even if S
is compromised in a fashion not readily detectable by the witnesses, CoSi still
guarantees S's exposure to public scrutiny, forcing secrecy-minded attackers to
risk that the compromise will soon be detected by one of the W witnesses.
Because clients can verify collective signatures efficiently without
communication, CoSi protects clients' privacy, and offers the first
transparency mechanism effective against persistent man-in-the-middle attackers
who control a victim's Internet access, the authority's secret key, and several
witnesses' secret keys. CoSi builds on existing cryptographic multisignature
methods, scaling them to support thousands of witnesses via signature
aggregation over efficient communication trees. A working prototype
demonstrates CoSi in the context of timestamping and logging authorities,
enabling groups of over 8,000 distributed witnesses to cosign authoritative
statements in under two seconds.Comment: 20 pages, 7 figure
Data Leak Detection As a Service: Challenges and Solutions
We describe a network-based data-leak detection (DLD)
technique, the main feature of which is that the detection
does not require the data owner to reveal the content of the
sensitive data. Instead, only a small amount of specialized
digests are needed. Our technique â referred to as the fuzzy
fingerprint â can be used to detect accidental data leaks due
to human errors or application flaws. The privacy-preserving
feature of our algorithms minimizes the exposure of sensitive
data and enables the data owner to safely delegate the
detection to others.We describe how cloud providers can offer
their customers data-leak detection as an add-on service
with strong privacy guarantees.
We perform extensive experimental evaluation on the privacy,
efficiency, accuracy and noise tolerance of our techniques.
Our evaluation results under various data-leak scenarios
and setups show that our method can support accurate
detection with very small number of false alarms, even
when the presentation of the data has been transformed. It
also indicates that the detection accuracy does not degrade
when partial digests are used. We further provide a quantifiable
method to measure the privacy guarantee offered by our
fuzzy fingerprint framework
On Formal Methods for Collective Adaptive System Engineering. {Scalable Approximated, Spatial} Analysis Techniques. Extended Abstract
In this extended abstract a view on the role of Formal Methods in System
Engineering is briefly presented. Then two examples of useful analysis
techniques based on solid mathematical theories are discussed as well as the
software tools which have been built for supporting such techniques. The first
technique is Scalable Approximated Population DTMC Model-checking. The second
one is Spatial Model-checking for Closure Spaces. Both techniques have been
developed in the context of the EU funded project QUANTICOL.Comment: In Proceedings FORECAST 2016, arXiv:1607.0200
Trust in social machines: the challenges
The World Wide Web has ushered in a new generation of applications constructively linking people and computers to create what have been called âsocial machines.â The âcomponentsâ of these machines are people and technologies. It has long been recognised that for people to participate in social machines, they have to trust the processes. However, the notions of trust often used tend to be imported from agent-based computing, and may be too formal, objective and selective to describe human trust accurately. This paper applies a theory of human trust to social machines research, and sets out some of the challenges to system designers
Consistent SDNs through Network State Fuzzing
The conventional wisdom is that a software-defined network (SDN) operates under the premise that the logically centralized control plane has an accurate representation of the actual data plane state. Nevertheless, bugs, misconfigurations, faults or attacks can introduce inconsistencies that undermine correct operation. Previous work in this area, however, lacks a holistic methodology to tackle this problem and thus, addresses only certain parts of the problem. Yet, the consistency of the overall system is only as good as its least consistent part. Motivated by an analogy of network consistency checking with program testing, we propose to add active probe-based network state fuzzing to our consistency check repertoire. Hereby, our system, PAZZ, combines production traffic with active probes to continuously test if the actual forwarding path and decision elements (on the data plane) correspond to the expected ones (on the control plane). Our insight is that active traffic covers the inconsistency cases beyond the ones identified by passive traffic. PAZZ prototype was built and evaluated on topologies of varying scale and complexity. Our results show that PAZZ requires minimal network resources to detect persistent data plane faults through fuzzing and localize them quickly
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