Finally, we elaborate on the implications of our findings from the perspective of better understanding the SMM community and improving their social welfare. Moreover, we demonstrate that by utilizing such correlations, it has the potential to construct machine-learning-based models for relationship status inference. Specifically, using a filtered dataset containing 2,359 active SMMSA users with their self-reported relationship status and publicly available app usage data, we explore the correlations between SMM’s relationship status and their online digital footprints on SMMSA and present a set of interesting findings. With the prevalence of SMM-oriented social apps (called SMMSA for short), this paper investigates the relationship status of SMM from a new perspective, that is, by introducing the SMM’s online digital footprints left on SMMSA (e.g., presented profile, social interactions, expressions, sentiment, and mobility trajectories). Furthermore, knowing SMM’s relationship and the correlations with other visible features is also beneficial for optimizing the social applications’ functionalities in terms of privacy preserving and friends recommendation. However, the relationship maintaining for them is also challenging due to the much less supports compared to the heterosexual couples, so that it is important to identify those SMM in steady relationship and provide corresponding personalized assistance. Maintaining steady relationships is beneficial to the wellbeing of SMM both mentally and physically. With the increasing social acceptance and openness, more and more sexual-minority men (SMM) have succeeded in creating and sustaining steady relationships in recent years. The reduced training complexity makes Auto-Key more practical for edge computing, which provides better privacy protection to biometric and behavioral data compared to cloud-based training. In the proposed framework, a subject-specific model can be trained with 50% fewer data and 88% less time by retraining a pre-trained general model when compared to training a new model from scratch. Our results show that, on average, Auto-Key increases the matching rate of independently generated bits from two sensors attached at two different locations by 16.5%, which speeds up the successful generation of fully-matching symmetric keys at independent wearable sensors by a factor of 1.9. We prototype the proposed method and evaluate it using a public acceleration dataset collected from 15 real subjects wearing accelerometers attached to seven different locations of the body. To address the challenge, we propose a novel machine learning framework, called Auto-Key, that uses an autoencoder to help one device predict the gait observations at another distant device attached to the same body and generate the key using the predicted sensor data. A key challenge for such gait-based key generation lies in matching the bits of the keys generated by independent devices despite the noisy sensor measurements, especially when the devices are located far apart on the body affected by different sources of noise. Since the conventional key distribution systems are too onerous for resource-constrained wearable sensors, researchers are pursuing a new light-weight key generation approach that enables two wearable devices attached at different locations of the user body to generate an identical key simultaneously simply from their independent observations of user gait. With the rising popularity of wearable devices and sensors, shielding Body Area Networks (BANs) from eavesdroppers has become an urgent problem to solve. We discuss our findings in the context of developing a theory of collective efficacy for security and privacy and new collaborative technologies that can reduce the barriers to social help. Furthermore, we show that familiarity with an older relative’s preferences is essential in providing meaningful support. Our findings point to the potential for helping older relatives, i.e., people are more willing to help and guide them than other social groups. To bridge this gap, we have conducted a mixed method study, qualitatively analyzing the helpers’ assistance stories and quantitatively estimating the factors that affect helpers’ willingness to offer assistance to older relatives regarding mobile security and privacy problems. While support from family and friends is known to be an effective enabler in older adults’ technology adoption, we know very little about the family members’ motivations for providing help, the context, and the process in which they provide it. Security and privacy pose a serious barrier to the use of mobile technology by older adults.
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