Adversarial Attacks on Online Learning to Rank with Stochastic Click Models

We propose the first study of adversarial attacks on online learning to rank. The goal of the adversary is to misguide the online learning to rank algorithm to place the target item on top of the ranking list linear times to time horizon $T$ with a sublinear attack cost. We propose generalized list poisoning attacks that perturb the ranking list presented to the user. This strategy can efficiently attack any no-regret ranker in general stochastic click models. Furthermore, we propose a click poisoning-based strategy named attack-then-quit that can efficiently attack two representative OLTR algorithms for stochastic click models. We theoretically analyze the success and cost upper bound of the two proposed methods. Experimental results based on synthetic and real-world data further validate the effectiveness and cost-efficiency of the proposed attack strategies

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

Data Poisoning Attacks in Contextual Bandits

We study offline data poisoning attacks in contextual bandits, a class of reinforcement learning problems with important applications in online recommendation and adaptive medical treatment, among others. We provide a general attack framework based on convex optimization and show that by slightly manipulating rewards in the data, an attacker can force the bandit algorithm to pull a target arm for a target contextual vector. The target arm and target contextual vector are both chosen by the attacker. That is, the attacker can hijack the behavior of a contextual bandit. We also investigate the feasibility and the side effects of such attacks, and identify future directions for defense. Experiments on both synthetic and real-world data demonstrate the efficiency of the attack algorithm.Comment: GameSec 201