In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

[Q]: It is clear that users should own and control their data and privacy. Utility providers are also becoming more interested in guaranteeing data privacy. Therefore, users and providers can and should collaborate in privacy protecting challenges, and this paper addresses this new paradigm. We propose a framework where the user controls what characteristics of the data they want to share (utility) and what they want to keep private (secret), without necessarily asking the utility provider to change its existing machine learning algorithms. We first analyze the space of privacy-preserving representations and derive natural information-theoretic bounds on the utility-privacy trade-off when disclosing a sanitized version of the data X. We present explicit learning architectures to learn privacy-preserving representations that approach this bound in a data-driven fashion. We describe important use-case scenarios where the utility providers are willing to collaborate with the sanitization process. We study space-preserving transformations where the utility provider can use the same algorithm on original and sanitized data, a critical and novel attribute to help service providers accommodate varying privacy requirements with a single set of utility algorithms. We illustrate this framework through  the implementation of three use cases; subject-within-subject, where we tackle the problem of having a face identity detector that works only on a consenting subset of users, an important application, for example, for mobile devices activated by face recognition; gender-and-subject, where we preserve facial verification while hiding the gender attribute for users who choose to do so; and emotion-and-gender, where we hide independent variables, as is the case of hiding gender while preserving emotion detection.
[A]: Learning privacy-preserving transformations from data. A collaborative approach


[Q]: With the rapid proliferation of IoT devices, our cyberspace is nowadays dominated by billions of low-cost computing nodes, which expose an unprecedented heterogeneity to our computing systems. Dynamic analysis, one of the most effective approaches to finding software bugs, has become paralyzed due to the lack of a generic emulator capable of running diverse previously-unseen firmware. In recent years, we have witnessed devastating security breaches targeting IoT devices. These security concerns have significantly hamstrung further evolution of IoT technology. In this work, we present Laelaps, a device emulator specifically designed to run diverse software on low-cost IoT devices. We do not encode into our emulator any specific information about a device. Instead, Laelaps infers the expected behavior of firmware via symbolic-execution-assisted peripheral emulation and generates proper inputs to steer concrete execution on the fly. This unique design feature makes Laelaps the first generic device emulator capable of running diverse firmware with no a priori knowledge about the target device. To demonstrate the capabilities of Laelaps, we deployed two popular dynamic analysis techniques---fuzzing testing and dynamic symbolic execution---on top of our emulator. We successfully identified both self-injected and real-world vulnerabilities.
[A]: Device-agnostic Firmware Execution


[Q]: We exploit a recently derived inversion scheme for arbitrary deep neural networks to develop a new semi-supervised learning framework that applies to a wide range of systems and problems.  
 The approach reaches current state-of-the-art methods on MNIST and provides reasonable performances on SVHN and CIFAR10. Through the introduced method, residual networks are for the first time applied to semi-supervised tasks. Experiments with one-dimensional signals highlight the generality of the method. Importantly, our approach is simple, efficient, and requires no change in the deep network architecture.
[A]:
We exploit an inversion scheme for arbitrary deep neural networks to develop a new semi-supervised learning framework applicable to many topologies.