226,088 research outputs found
Membership has its Privileges - The Effect of Membership in International Organizations on FDI
We argue that membership in International Organizations (IOs) is an important determinant of FDI inflows. To the extent that membership restricts a country from pursuing policies that are harmful to investors, it can signal low political risk. Using data over the 1971-2005 period, we find that membership in IOs does indeed increase inflows of FDI. Controlling for the endogeneity of membership, we find this effect to be substantively important and robust to the method of estimation.membership in international organizations, FDI, investment climate, political risk, signaling, separating equilibrium
Common Representation of Information Flows for Dynamic Coalitions
We propose a formal foundation for reasoning about access control policies
within a Dynamic Coalition, defining an abstraction over existing access
control models and providing mechanisms for translation of those models into
information-flow domain. The abstracted information-flow domain model, called a
Common Representation, can then be used for defining a way to control the
evolution of Dynamic Coalitions with respect to information flow
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
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