Cross cultural perspectives of decision-making and control in multinational corporations operating in ASEAN

Kajian ini membincangkan isu-isu persekitaran budaya negara ASEAN dan cabaran-cabarannya terhadap pengurus-pengurus korporat multi-nasional. Kajian ini melaporkan penemuan terhadap corak membuat keputusan, kawalan dan pengurusan budaya serta aspek gelagat. Walaupun penemuan ini mempunyai persamaan dengan kajian lain, namun terdapat perbezaan dalam corak membuat keputusan, kawalan dan pengurusan budaya serta aspek gelagat

Multi-party Poisoning through Generalized pp-Tampering

In a poisoning attack against a learning algorithm, an adversary tampers with a fraction of the training data $T$ with the goal of increasing the classification error of the constructed hypothesis/model over the final test distribution. In the distributed setting, $T$ might be gathered gradually from $m$ data providers $P_1,\dots,P_m$ who generate and submit their shares of $T$ in an online way. In this work, we initiate a formal study of $(k,p)$-poisoning attacks in which an adversary controls $k\in[n]$ of the parties, and even for each corrupted party $P_i$, the adversary submits some poisoned data $T'_i$ on behalf of $P_i$ that is still "$(1-p)$-close" to the correct data $T_i$ (e.g., $1-p$ fraction of $T'_i$ is still honestly generated). For $k=m$, this model becomes the traditional notion of poisoning, and for $p=1$ it coincides with the standard notion of corruption in multi-party computation. We prove that if there is an initial constant error for the generated hypothesis $h$, there is always a $(k,p)$-poisoning attacker who can decrease the confidence of $h$ (to have a small error), or alternatively increase the error of $h$, by $\Omega(p \cdot k/m)$. Our attacks can be implemented in polynomial time given samples from the correct data, and they use no wrong labels if the original distributions are not noisy. At a technical level, we prove a general lemma about biasing bounded functions $f(x_1,\dots,x_n)\in[0,1]$ through an attack model in which each block $x_i$ might be controlled by an adversary with marginal probability $p$ in an online way. When the probabilities are independent, this coincides with the model of $p$-tampering attacks, thus we call our model generalized $p$-tampering. We prove the power of such attacks by incorporating ideas from the context of coin-flipping attacks into the $p$-tampering model and generalize the results in both of these areas