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Found 12 papers, 5 papers with code
Learning Differentially Private Mechanisms
We demonstrate that our approach is able to learn foundational algorithms from the differential privacy literature and significantly outperforms natural program synthesis baselines.
Analyzing Accuracy Loss in Randomized Smoothing Defenses
In test-time attacks an adversary crafts adversarial examples, which are specially crafted perturbations imperceptible to humans which, when added to an input example, force a machine learning model to misclassify the given input example.
Advances and Open Problems in Federated Learning
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
Hypothesis Testing Interpretations and Renyi Differential Privacy
These conditions are useful to analyze the distinguishability power of divergences and we use them to study the hypothesis testing interpretation of some relaxations of differential privacy based on Renyi divergence.
Data Poisoning against Differentially-Private Learners: Attacks and Defenses
Data poisoning attacks aim to manipulate the model produced by a learning algorithm by adversarially modifying the training set.
Fake News Detection via NLP is Vulnerable to Adversarial Attacks
In this paper, we argue that these models have the potential to misclassify fact-tampering fake news as well as under-written real news.
Do Prices Coordinate Markets?
Second, we provide learning-theoretic results that show that such prices are robust to changing the buyers in the market, so long as all buyers are sampled from the same (unknown) distribution.
Really Natural Linear Indexed Type Checking
A natural way to enhance the expressiveness of this approach is by allowing the indices to depend on runtime information, in the spirit of dependent types.
Logic in Computer Science
Computer-aided verification in mechanism design
To address both concerns, we explore techniques from computer-aided verification to construct formal proofs of incentive properties.
Computer Science and Game Theory Logic in Computer Science
Higher-Order Approximate Relational Refinement Types for Mechanism Design and Differential Privacy
Unlike typical programmatic properties, it is not sufficient for algorithms to merely satisfy the property---incentive properties are only useful if the strategic agents also believe this fact.
Programming Languages Computer Science and Game Theory
Privately Solving Linear Programs
In this paper, we initiate the systematic study of solving linear programs under differential privacy.
Dual Query: Practical Private Query Release for High Dimensional Data
We present a practical, differentially private algorithm for answering a large number of queries on high dimensional datasets.
