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  1. Label differential privacy is a relaxation of differential privacy for machine learning scenarios where the labels are the only sensitive information that needs to be protected in the training data. For example, imagine a survey from a participant in a university class about their vaccination status. Some attributes of the students are publicly available but their vaccination status is sensitive information and must remain private. Now if we want to train a model that predicts whether a student has received vaccination using only their public information, we can use label-DP. Recent works on label-DP use different ways of adding noise to the labels in order to obtain label-DP models. In this work, we present novel techniques for training models with label-DP guarantees by leveraging unsupervised learning and semi-supervised learning, enabling us to inject less noise while obtaining the same privacy, therefore achieving a better utility-privacy trade-off. We first introduce a framework that starts with an unsupervised classifier f0 and dataset D with noisy label set Y , reduces the noise in Y using f0 , and then trains a new model f using the less noisy dataset. Our noise reduction strategy uses the model f0 to remove the noisy labelsmore »that are incorrect with high probability. Then we use semi-supervised learning to train a model using the remaining labels. We instantiate this framework with multiple ways of obtaining the noisy labels and also the base classifier. As an alternative way to reduce the noise, we explore the effect of using unsupervised learning: we only add noise to a majority voting step for associating the learned clusters with a cluster label (as opposed to adding noise to individual labels); the reduced sensitivity enables us to add less noise. Our experiments show that these techniques can significantly outperform the prior works on label-DP.« less
    Free, publicly-accessible full text available October 1, 2023
  2. An attacker can obtain a valid TLS certificate for a domain by hijacking communication between a certificate authority (CA) and a victim domain. Performing domain validation from multiple vantage points can defend against these attacks. We explore the design space of multi-vantage-point domain validation to achieve (1) security via sufficiently diverse vantage points, (2) performance by ensuring low latency and overhead in certificate issuance, (3) manageability by complying with CA/Browser forum requirements, and requiring minimal changes to CA operations, and (4) a low benign failure rate for legitimate requests. Our opensource implementation was deployed by the Let's Encrypt CA in February 2020, and has since secured the issuance of more than half a billion certificates during the first year of its deployment. Using real-world operational data from Let's Encrypt, we show that our approach has negligible latency and communication overhead, and a benign failure rate comparable to conventional designs with one vantage point. Finally, we evaluate the security improvements using a combination of ethically conducted real-world BGP hijacks, Internet-scale traceroute experiments, and a novel BGP simulation framework. We show that multi-vantage-point domain validation can thwart the vast majority of BGP attacks. Our work motivates the deployment of multi-vantage-point domain validationmore »across the CA ecosystem to strengthen TLS certificate issuance and user privacy.« less
  3. Abstract The popularity of Tor has made it an attractive target for a variety of deanonymization and fingerprinting attacks. Location-based path selection algorithms have been proposed as a countermeasure to defend against such attacks. However, adversaries can exploit the location-awareness of these algorithms by strategically placing relays in locations that increase their chances of being selected as a client’s guard. Being chosen as a guard facilitates website fingerprinting and traffic correlation attacks over extended time periods. In this work, we rigorously define and analyze the guard placement attack . We present novel guard placement attacks and show that three state-of-the-art path selection algorithms—Counter-RAPTOR, DeNASA, and LASTor—are vulnerable to these attacks, overcoming defenses considered by all three systems. For instance, in one attack, we show that an adversary contributing only 0.216% of Tor’s total bandwidth can attain an average selection probability of 18.22%, 84× higher than what it would be under Tor currently. Our findings indicate that existing location-based path selection algorithms allow guards to achieve disproportionately high selection probabilities relative to the cost required to run the guard. Finally, we propose and evaluate a generic defense mechanism that provably defends any path selection algorithm against guard placement attacks. We runmore »our defense mechanism on each of the three path selection algorithms, and find that our mechanism significantly enhances the security of these algorithms against guard placement attacks with only minimal impact to the goals or performance of the original algorithms.« less
  4. Website fingerprinting attacks, which use statistical analysis on network traffic to compromise user privacy, have been shown to be effective even if the traffic is sent over anonymity-preserving networks such as Tor. The classical attack model used to evaluate website fingerprinting attacks assumes an on-path adversary, who can observe all traffic traveling between the user’s computer and the secure network. In this work we investigate these attacks under a different attack model, in which the adversary is capable of sending a small amount of malicious JavaScript code to the target user’s computer. The malicious code mounts a cache side-channel attack, which exploits the effects of contention on the CPU’s cache, to identify other websites being browsed. The effectiveness of this attack scenario has never been systematically analyzed, especially in the open-world model which assumes that the user is visiting a mix of both sensitive and non-sensitive sites. We show that cache website fingerprinting attacks in JavaScript are highly feasible. Specifically, we use machine learning techniques to classify traces of cache activity. Unlike prior works, which try to identify cache conflicts, our work measures the overall occupancy of the last-level cache. We show that our approach achieves high classification accuracy inmore »both the open-world and the closed-world models. We further show that our attack is more resistant than network-based fingerprinting to the effects of response caching, and that our techniques are resilient both to network-based defenses and to side-channel countermeasures introduced to modern browsers as a response to the Spectre attack. To protect against cache-based website fingerprinting, new defense mechanisms must be introduced to privacy-sensitive browsers and websites. We investigate one such mechanism, and show that generating artificial cache activity reduces the effectiveness of the attack and completely eliminates it when used in the Tor Browser« less