3,357 research outputs found
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
Machine learning algorithms, when applied to sensitive data, pose a distinct
threat to privacy. A growing body of prior work demonstrates that models
produced by these algorithms may leak specific private information in the
training data to an attacker, either through the models' structure or their
observable behavior. However, the underlying cause of this privacy risk is not
well understood beyond a handful of anecdotal accounts that suggest overfitting
and influence might play a role.
This paper examines the effect that overfitting and influence have on the
ability of an attacker to learn information about the training data from
machine learning models, either through training set membership inference or
attribute inference attacks. Using both formal and empirical analyses, we
illustrate a clear relationship between these factors and the privacy risk that
arises in several popular machine learning algorithms. We find that overfitting
is sufficient to allow an attacker to perform membership inference and, when
the target attribute meets certain conditions about its influence, attribute
inference attacks. Interestingly, our formal analysis also shows that
overfitting is not necessary for these attacks and begins to shed light on what
other factors may be in play. Finally, we explore the connection between
membership inference and attribute inference, showing that there are deep
connections between the two that lead to effective new attacks
ANCHOR: logically-centralized security for Software-Defined Networks
While the centralization of SDN brought advantages such as a faster pace of
innovation, it also disrupted some of the natural defenses of traditional
architectures against different threats. The literature on SDN has mostly been
concerned with the functional side, despite some specific works concerning
non-functional properties like 'security' or 'dependability'. Though addressing
the latter in an ad-hoc, piecemeal way, may work, it will most likely lead to
efficiency and effectiveness problems. We claim that the enforcement of
non-functional properties as a pillar of SDN robustness calls for a systemic
approach. As a general concept, we propose ANCHOR, a subsystem architecture
that promotes the logical centralization of non-functional properties. To show
the effectiveness of the concept, we focus on 'security' in this paper: we
identify the current security gaps in SDNs and we populate the architecture
middleware with the appropriate security mechanisms, in a global and consistent
manner. Essential security mechanisms provided by anchor include reliable
entropy and resilient pseudo-random generators, and protocols for secure
registration and association of SDN devices. We claim and justify in the paper
that centralizing such mechanisms is key for their effectiveness, by allowing
us to: define and enforce global policies for those properties; reduce the
complexity of controllers and forwarding devices; ensure higher levels of
robustness for critical services; foster interoperability of the non-functional
property enforcement mechanisms; and promote the security and resilience of the
architecture itself. We discuss design and implementation aspects, and we prove
and evaluate our algorithms and mechanisms, including the formalisation of the
main protocols and the verification of their core security properties using the
Tamarin prover.Comment: 42 pages, 4 figures, 3 tables, 5 algorithms, 139 reference
On the Functional Test of Special Function Units in GPUs
The Graphics Processing Units (GPUs) usage has extended from graphic applications to others where their high computational power is exploited (e.g., to implement Artificial Intelligence algorithms). These complex applications usually need highly intensive computations based on floating-point transcendental functions. GPUs may efficiently compute these functions in hardware using ad hoc Special Function Units (SFUs). However, a permanent fault in such units could be very critical (e.g., in safety-critical automotive applications). Thus, test methodologies for SFUs are strictly required to achieve the target reliability and safety levels. In this work, we present a functional test method based on a Software-Based Self-Test (SBST) approach targeting the SFUs in GPUs. This method exploits different approaches to build a test program and applies several optimization strategies to exploit the GPU parallelism to speed up the test procedure and reduce the required memory. The effectiveness of this methodology was proven by resorting to an open-source GPU model (FlexGripPlus) compatible with NVIDIA GPUs. The experimental results show that the proposed technique achieves 90.75% of fault coverage and up to 94.26% of Testable Fault Coverage, reducing the required memory and test duration with respect to pseudorandom strategies proposed by other authors
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