Abstract:Studies have shown that children can be exposed to smart devices at a very early age. This has important implications on research in children-computer interaction, children online safety and early education. Many systems have been built based on such research. In this work, we present multiple techniques to automatically detect the presence of a child on a smart device, which could be used as the first step on such systems. Our methods distinguish children from adults based on behavioral differences while oper… Show more
“…To determine the pattern range, the lowest and highest start positions of size 0.48923634 frequencies in the 5 data sets are taken. From Table8, the pattern-range is (10)(11)(12)(13)(14). = 1 passes the 0.7 threshold.…”
Section: Methodsmentioning
confidence: 99%
“…Ref. [11] is another study where they handled distinguishing between soft and hard finger pressure, based on users' features such as small fingers and speed. [12] proposes another powerful Android software development framework that uses OpenCV library for real-time face recognition.…”
This study introduces a novel authentication methodology; it is based on pattern recognition of fingers size and pressure when users touch smartphone screen. By analyzing diagrams of these touches and applying data mining for the first time as an authentication technique, this paper presents three new approaches. First, an exact-range evaluation approach has been verified that size is more recognition consistency than pressure. Second, a pattern-range is a new technique reliance on size frequency position. At last, using a size-range has been facilitated the login. The association rules have been modified to work on finger touchscreen data files. To login, 94.1111% of 18 authorized users are succeeded and 98.9% of 20 unauthorized users are failed. Android device and Android studio are used. Size and pressure are normalized to 1; a training set is applied; the password is not considered.
“…To determine the pattern range, the lowest and highest start positions of size 0.48923634 frequencies in the 5 data sets are taken. From Table8, the pattern-range is (10)(11)(12)(13)(14). = 1 passes the 0.7 threshold.…”
Section: Methodsmentioning
confidence: 99%
“…Ref. [11] is another study where they handled distinguishing between soft and hard finger pressure, based on users' features such as small fingers and speed. [12] proposes another powerful Android software development framework that uses OpenCV library for real-time face recognition.…”
This study introduces a novel authentication methodology; it is based on pattern recognition of fingers size and pressure when users touch smartphone screen. By analyzing diagrams of these touches and applying data mining for the first time as an authentication technique, this paper presents three new approaches. First, an exact-range evaluation approach has been verified that size is more recognition consistency than pressure. Second, a pattern-range is a new technique reliance on size frequency position. At last, using a size-range has been facilitated the login. The association rules have been modified to work on finger touchscreen data files. To login, 94.1111% of 18 authorized users are succeeded and 98.9% of 20 unauthorized users are failed. Android device and Android studio are used. Size and pressure are normalized to 1; a training set is applied; the password is not considered.
“…The authors exploited the k Nearest Neighbours (k-NN) algorithm, obtaining an accuracy of 85.3%. Similarly, Nguyen et al [115] developed a method to distinguish an adult from a child exploiting the behavioural differences captured by the motion sensors. Based on the hypothesis that children, with smaller hands, will tend to be more shaky, they achieved an accuracy of 96% using the Random Forest (RF) method.…”
Section: A Demographicsmentioning
confidence: 99%
“…The authors used touch interaction information to classify children into three groups aged 18 months to 8 years old. Nguyen et al [115] also conducted a study using RF on tap gestures to distinguish between an adult and a child, achieving an accuracy of 99%. Touchscreen data has also been used to extract a person's gender.…”
The number of mobile devices, such as smartphones and smartwatches, is relentlessly increasing to almost 6.8 billion by 2022, and along with it, the amount of personal and sensitive data captured by them. This survey overviews the state of the art of what personal and sensitive user attributes can be extracted from mobile device sensors, emphasising critical aspects such as demographics, health and body features, activity and behaviour recognition, etc. In addition, we review popular metrics in the literature to quantify the degree of privacy, and discuss powerful privacy methods to protect the sensitive data while preserving data utility for analysis. Finally, open research questions are presented for further advancements in the field.
“…Early studies [24,96] used high-dimensional features, and the main correlating feature for the specific attribute we study (gender) was the raw accelerometer trace. Moreover, in the recent past, many studies have been published regarding inferring demographic attributes such as gender and age [42,62,89] using raw accelerometer and gyroscope traces of wearables and mobile phones. In these papers, sensor data are again high-dimensional and raw, and need to be transformed in many ways to engineer features.…”
Smartphone applications that use passive sensing to support human health and well-being primarily rely on: (a) generating lowdimensional representations from high-dimensional data streams; (b) making inferences regarding user behavior; and (c) using those inferences to benefit application users. Meanwhile, sometimes these datasets are shared with third parties as well. Human-centered ubiquitous systems need to ensure that sensitive attributes of users are protected when applications provide utility to people based on such behavioral inferences. In this paper, we demonstrate that inferences of sensitive attributes of users (gender, body mass index category) are possible using low-dimensional and sparse data coming from mobile food diaries (a combination of sensor data and self-reports). After exposing this potential risk, we demonstrate how deep learning techniques can be used for feature transformation to preserve sensitive user information while achieving high accuracies for application-related inferences (e.g. inferring the type of consumed food). Our work is based on two datasets of daily eating behavior of 160 young adults from Switzerland (N =122) and Mexico (N =38). Results show that using the proposed approach, accuracies in the order of 75%-90% can be achieved for application related inferences, while reducing the sensitive inference to almost random performance.
CCS CONCEPTS• Human-centered computing → Mobile computing; Smartphones; Mobile phones; Empirical studies in ubiquitous and mobile computing; • Social and professional topics → Gender; • Applied computing → Consumer health; Health informatics; Sociology.
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