MixNN: Protection of Federated Learning Against Inference Attacks by Mixing Neural Network Layers

International audienceMachine Learning (ML) has emerged as a core technology to provide learning models to perform complex tasks. Boosted by Machine Learning as a Service (MLaaS), the number of applications relying on ML capabilities is ever increasing. However, ML models are the source of different privacy violations through passive or active attacks from different entities. In this paper, we present MixNN a proxy-based privacy-preserving system for federated learning to protect the privacy of participants against a curious or malicious aggregation server trying to infer sensitive information (i.e., membership and attribute inferences). MixNN receives the model updates from participants and mixes layers between participants before sending the mixed updates to the aggregation server. This mixing strategy drastically reduces privacy leaks without any trade-off with utility. Indeed, mixing the updates of the model has no impact on the result of the aggregation of the updates computed by the server. We report on an extensive evaluation of MixNN using several datasets and neural networks architectures to quantify privacy leakage through membership and attribute inference attacks as well the robustness of the protection. We show that MixNN significantly limits both the membership and attribute inferences compared to a baseline using model compression and noisy gradient (well known to damage the utility) while keeping the same level of utility as classic federated learning

The Influence of Traffic Structure on Airspace Capacity

Best paper award for the Network Management trackInternational audienceAirspace structure can be used as a procedural mechanism for a priori separation and organization of en-route air traffic. Although many studies have explored novel structuring methods to increase en-route airspace capacity, the relationship between the level of structuring of traffic and airspace capacity is not well established. To better understand the influence of traffic structure on airspace capacity, in this research, four airspace concepts, representing discrete points along the dimension of structure, were compared using large-scale simulation experiments. By subjecting the concepts to multiple traffic demand scenarios, the structure-capacity relationship was inferred from the effect of traffic demand variations on safety, efficiency and stability metrics. These simulations were performed within the context of a future personal aerial transportation system, and considered both nominal and non-nominal conditions. Simulation results suggest that the structuring of traffic must take into account the expected traffic demand pattern to be beneficial in terms of capacity. Furthermore, for the heterogeneous, or uniformly distributed, traffic demand patterns considered in this work, a decentralized layered airspace concept, in which each altitude band limited horizontal travel to within a predefined heading range, led to the best balance of all the metrics considered

MixNN: Protection of Federated Learning Against Inference Attacks by Mixing Neural Network Layers

International audienceMachine Learning (ML) has emerged as a core technology to provide learning models to perform complex tasks. Boosted by Machine Learning as a Service (MLaaS), the number of applications relying on ML capabilities is ever increasing. However, ML models are the source of different privacy violations through passive or active attacks from different entities. In this paper, we present MixNN a proxy-based privacy-preserving system for federated learning to protect the privacy of participants against a curious or malicious aggregation server trying to infer sensitive information (i.e., membership and attribute inferences). MixNN receives the model updates from participants and mixes layers between participants before sending the mixed updates to the aggregation server. This mixing strategy drastically reduces privacy leaks without any trade-off with utility. Indeed, mixing the updates of the model has no impact on the result of the aggregation of the updates computed by the server. We report on an extensive evaluation of MixNN using several datasets and neural networks architectures to quantify privacy leakage through membership and attribute inference attacks as well the robustness of the protection. We show that MixNN significantly limits both the membership and attribute inferences compared to a baseline using model compression and noisy gradient (well known to damage the utility) while keeping the same level of utility as classic federated learning

Exemplification case studies as a focus for the implementation of best practices related to aircraft noise management at airports

This study presents the analysis of six airport exemplification case studies undertaken in the European project “Aviation Noise Impact Management through Novel Approaches - ANIMA”. Best practices related to aircraft noise management at airports in individual airport contexts were implemented and evaluated. Case studies on communication and community engagement in airport noise management were investigated at Heathrow (United Kingdom), Ljubljana (Slovenia) and Rotterdam The Hague (The Netherlands) airports. For Zaporizhzhia (Ukraine) and Iasi (Romania) airports, the implementation of interventions related to land-use planning was examined. The interdependencies between noise and emissions were studied for Cluj (Romania) airport. All case studies were performed under the scope of the corresponding national legislation and guidelines. Individual characteristics of airport operations were taken into account. The case studies were aligned with expectations and priorities of all involved stakeholders, such as representatives of airport operators, local communities, civil aviation authorities and policy makers. The efficacy of the noise management case studies is assessed in terms of: the capacity to negotiate consensus outcomes, the extent to which noise impact reductions were achieved; and the participants' satisfaction with the process and outcomes. Experience gained from these studies will be used to distill best practices for future interventions

ANIMA D2.11: recommendations from exemplification case studies summary and implications for BP dissemination

This study presents the analysis of seven airport exemplification case studies undertaken in the European project “Aviation Noise Impact Management through Novel Approaches – ANIMA”. Best practices related to aircraft noise management at airports in individual airport contexts were implemented and evaluated. Case studies on communication and community engagement in airport noise management were investigated at Heathrow (United Kingdom), Ljubljana (Slovenia) and Rotterdam The Hague (The Netherlands) airports. For Zaporizhzhia (Ukraine) and Iasi (Romania) airports, the implementation of interventions related to land use planning was examined. The interdependencies between noise and emissions were studied for the airport at Cluj (Romania) along with an exploration of key Quality of Life issues. All case studies were performed under the scope of the corresponding national legislation and guidelines. Individual characteristics of airport operations were taken into account. The case studies were aligned with expectations and priorities of all involved stakeholders, such as representatives of airport operators, local communities, civil aviation authorities and policy makers. The efficacy of the noise management case studies was assessed in terms of the capacity to negotiate consensus outcomes, the extent to which noise impact reductions were achieved, and the participants’ satisfaction with the process and outcomes. The case studies revealed the vital importance of community engagement in noise management if decisions are to be accepted and outcomes valued. In general, the earlier this engagement starts in the process of decision-making and implementation the better; although care needs to be taken in the selection of methods of engagement to ensure the tools used are appropriate to the engagement and communication task faced. In this way, overly long engagement should be avoided and with that the risk of increased uncertainty in outcomes. Such engagement should also ensure that decisions and subsequent interventions are tailored to local community concerns reflecting national, regional and cultural differences across Europe

Overview of Still-picture and VideoCompression Standards

This paper discusses current video compression techniques and presents an overview of the existing video compression standards and standards to be. The standards discussed here are standards defined by dedicated committees as well as de facto standards. 1 Introduction In the past years a number of compression standards have emerged and a number is now being developed. Although it would be useful to use only one general video compression standard, a growing number of standards is developed because of enhanced processing power, dedicated hardware, new compression techniques, and networks with different bandwidths. Each compression standard supports a specific video application. It is difficult to choose the correct compression standard for a specific application. As is true of compression in general that there does not exist one best compression algorithm, the same is true of video compression: there is no best standard. Some applications require fast real-time encoding, at the cost o..