Abstract

Abstract
Runtime permission managers for mobile devices allow requests to be performed at the time in which permissions are required, thus enabling the user to grant/deny requests in context according to their expectations. However, in order to avoid cognitive overload, second and subsequent requests are usually automatically granted without user intervention/awareness. This paper explores whether these automated decisions fit user expectations. We performed a field study with 93 participants to collect their privacy decisions, the surrounding context and whether each request was expected. The collected 65261 permission decisions revealed a strong misalignment between apps' practices and expectation as almost half of requests are unexpected by users. This ratio strongly varies with the requested permission, the category and visibility of the requesting application and the user itself; that is, expectation is subjective to each individual. Moreover, privacy decisions are most strongly correlated with user expectation, but such correlation is also highly personal. Finally, Android's default permission manager would have violated the privacy of our participants 15% of the time. © 2022 IEEE.

Abstract
Permission managers in mobile devices allow users to control permissions requests, by granting of denying application's access to data and sensors. However, existing managers are ineffective at both protecting and warning users of the privacy risks of their permissions' decisions. Recent research proposes privacy protection mechanisms through user profiles to automate privacy decisions, taking personal privacy preferences into consideration. While promising, these proposals usually resort to a centralized server towards training the automation model, thus requiring users to trust this central entity. In this paper we propose a methodology to build privacy profiles and train neural networks for prediction of privacy decisions, while guaranteeing user privacy, even against a centralized server. Specifically, we resort to privacy-preserving clustering techniques towards building the privacy profiles, that is, the server computes the centroids (profiles) without access to the underlying data. Then, using federated learning, the model to predict permission decisions is learnt in a distributed fashion while all data remains locally in the users' devices. Experiments following our methodology show the feasibility of building a personalized and automated permission manager guaranteeing user privacy, while also reaching a performance comparable to the centralized state of the art, with an F1-score of 0.9. © 2022 ACM.

Abstract
In this paper, we study the impact of computational complexity on the throughput limits of the fast Fourier transform (FFT) algorithm for orthogonal frequency division multiplexing (OFDM) waveforms. Based on the spectro-computational complexity (SC) analysis, we verify that the complexity of an N-point FFT grows faster than the number of bits in the OFDM symbol. Thus, we show that FFT nullifies the OFDM throughput on N unless the N -point discrete Fourier transform (DFT) problem verifies as Omega(N) , which remains a fascinating open question in theoretical computer science. Also, because FFT demands N to be a power of two 2(i) (i > 0), the spectrum widening leads to an exponential complexity on i , i.e. O (2(i)i) . To overcome these limitations, we consider the alternative frequency-time transform formulation of vector OFDM (V-OFDM), in which an N -point FFT is replaced by N/L (L > 0) smaller L-point FFTs to mitigate the cyclic prefix overhead of OFDM. Building on that, we replace FFT by the straightforward DFT algorithm to release the V-OFDM parameters from growing as powers of two and to benefit from flexible numerology (e.g., L = 3 , N = 156). Besides, by setting L to Theta (1) , the resulting solution can run linearly on N (rather than exponentially on i) while sustaining a non null throughput as N grows.

Abstract
The multitude of applications and security configurations of mobile devices requires automated approaches for effective user privacy protection. Current permission managers, the core mechanism for privacy protection in smartphones, have shown to be ineffective by failing to account for privacy's contextual dependency and personal preferences within context. In this paper we focus on the relation between privacy decisions (e.g. grant or deny a permission request) and their surrounding context, through an analysis of a real world dataset obtained in campaigns with 93 users. We leverage such findings and the collected data to develop methods for automated, personalized and context-aware privacy protection, so as to predict users' preferences with respect to permission requests. Our analysis reveals that while contextual features have some relevance in privacy decisions, the increase in prediction performance of using such features is minimal, since two features alone are capable of capturing a relevant effect of context changes, namely the category of the requesting application and the requested permission. Our methods for prediction of privacy preferences achieved an F1 score of 0.88, while reducing the number of privacy violations by 28% when compared to the standard Android permission manager.