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Register a new userPrivacy Assessment of Federated Learning using Private Personalized Layers
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Added by
Antoine Boutet
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Federated Learning (FL) is a collaborative scheme to train a learning model across multiple participants without sharing data. While FL is a clear step forward towards enforcing users privacy, different inference attacks have been developed. In this paper, we quantify the utility and privacy trade-off of a FL scheme using private personalized layers. While this scheme has been proposed as local adaptation to improve the accuracy of the model through local personalization, it has also the advantage to minimize the information about the model exchanged with the server. However, the privacy of such a scheme has never been quantified. Our evaluations using motion sensor dataset show that personalized layers speed up the convergence of the model and slightly improve the accuracy for all users compared to a standard FL scheme while better preventing both attribute and membership inferences compared to a FL scheme using local differential privacy.
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The prevalence of e-commerce has made detailed customers personal information readily accessible to retailers, and this information has been widely used in pricing decisions. When involving personalized information, how to protect the privacy of such information becomes a critical issue in practice. In this paper, we consider a dynamic pricing problem over $T$ time periods with an emph{unknown} demand function of posted price and personalized information. At each time $t$, the retailer observes an arriving customers personal information and offers a price. The customer then makes the purchase decision, which will be utilized by the retailer to learn the underlying demand function. There is potentially a serious privacy concern during this process: a third party agent might infer the personalized information and purchase decisions from price changes from the pricing system. Using the fundamental framework of differential privacy from computer science, we develop a privacy-preserving dynamic pricing policy, which tries to maximize the retailer revenue while avoiding information leakage of individual customers information and purchasing decisions. To this end, we first introduce a notion of emph{anticipating} $(varepsilon, delta)$-differential privacy that is tailored to dynamic pricing problem. Our policy achieves both the privacy guarantee and the performance guarantee in terms of regret. Roughly speaking, for $d$-dimensional personalized information, our algorithm achieves the expected regret at the order of $tilde{O}(varepsilon^{-1} sqrt{d^3 T})$, when the customers information is adversarially chosen. For stochastic personalized information, the regret bound can be further improved to $tilde{O}(sqrt{d^2T} + varepsilon^{-2} d^2)$
As artificial intelligence (AI)-empowered applications become widespread, there is growing awareness and concern for user privacy and data confidentiality. This has contributed to the popularity of federated learning (FL). FL applications often face data distribution and device capability heterogeneity across data owners. This has stimulated the rapid development of Personalized FL (PFL). In this paper, we complement existing surveys, which largely focus on the methods and applications of FL, with a review of recent advances in PFL. We discuss hurdles to PFL under the current FL settings, and present a unique taxonomy dividing PFL techniques into data-based and model-based approaches. We highlight their key ideas, and envision promising future trajectories of research towards new PFL architectural design, realistic PFL benchmarking, and trustworthy PFL approaches.
Federated analytics has many applications in edge computing, its use can lead to better decision making for service provision, product development, and user experience. We propose a Bayesian approach to trend detection in which the probability of a keyword being trendy, given a dataset, is computed via Bayes Theorem; the probability of a dataset, given that a keyword is trendy, is computed through secure aggregation of such conditional probabilities over local datasets of users. We propose a protocol, named SAFE, for Bayesian federated analytics that offers sufficient privacy for production grade use cases and reduces the computational burden of users and an aggregator. We illustrate this approach with a trend detection experiment and discuss how this approach could be extended further to make it production-ready.
Federated Learning (FL) allows multiple participants to train machine learning models collaboratively by keeping their datasets local and only exchanging model updates. Alas, recent work highlighted several privacy and robustness weaknesses in FL, presenting, respectively, membership/property inference and backdoor attacks. In this paper, we investigate to what extent Differential Privacy (DP) can be used to protect not only privacy but also robustness in FL. We present a first-of-its-kind empirical evaluation of Local and Central Differential Privacy (LDP/CDP) techniques in FL, assessing their feasibility and effectiveness. We show that both DP variants do defend against backdoor attacks, with varying levels of protection and utility, and overall much more effectively than previously proposed defenses. They also mitigate white-box membership inference attacks in FL, and our work is the first to show how effectively; neither, however, provides viable defenses against property inference. Our work also provides a re-usable measurement framework to quantify the trade-offs between robustness/privacy and utility in differentially private FL.
Recently, a number of backdoor attacks against Federated Learning (FL) have been proposed. In such attacks, an adversary injects poisoned model updates into the federated model aggregation process with the goal of manipulating the aggregated model to provide false predictions on specific adversary-chosen inputs. A number of defenses have been proposed; but none of them can effectively protect the FL process also against so-called multi-backdoor attacks in which multiple different backdoors are injected by the adversary simultaneously without severely impacting the benign performance of the aggregated model. To overcome this challenge, we introduce FLGUARD, a poisoning defense framework that is able to defend FL against state-of-the-art backdoor attacks while simultaneously maintaining the benign performance of the aggregated model. Moreover, FL is also vulnerable to inference attacks, in which a malicious aggregator can infer information about clients training data from their model updates. To thwart such attacks, we augment FLGUARD with state-of-the-art secure computation techniques that securely evaluate the FLGUARD algorithm. We provide formal argumentation for the effectiveness of our FLGUARD and extensively evaluate it against known backdoor attacks on several datasets and applications (including image classification, word prediction, and IoT intrusion detection), demonstrating that FLGUARD can entirely remove backdoors with a negligible effect on accuracy. We also show that private FLGUARD achieves practical runtimes.
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