Abstract While the public claim concern for their privacy, they frequently appear to overlook it. This disparity between concern and behaviour is known as the Privacy Paradox. Such issues are particularly prevalent on wearable devices. These products can store personal data, such as text messages and contact details. However, owners rarely use protective features. Educational games can be effective in encouraging changes in behaviour. Therefore, we developed the first privacy game for (Android) Wear OS watches. 10 participants used smartwatches for two months, allowing their high-level settings to be monitored. Five individuals were randomly assigned to our treatment group, and they played a dynamically-customised privacy-themed game. To minimise confounding variables, the other five received the same app but lacking the privacy topic. The treatment group improved their protection, with their usage of screen locks significantly increasing ( p = 0.043). In contrast, 80 of the control group continued to never restrict their settings. After the posttest phase, we evaluated behavioural rationale through semi-structured interviews. Privacy concerns became more nuanced in the treatment group, with opinions aligning with behaviour. Actions appeared influenced primarily by three factors: convenience, privacy salience and data sensitivity. This is the first smartwatch game to encourage privacy-protective behaviour.
Immaculacy @cite_74 is a proposed privacy game, in which the user faces dystopian scenarios. Characters progress through challenges by undertaking privacy-protective actions. This encourages reflection on behaviour, and we adopt a similar approach. Vaidya2014 considered interactive techniques to teach privacy. Since privacy is inherently complex, they recommended that scenarios be used. We implement scenario-based challenges, developing the first smartwatch privacy game.
@cite_74
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With the intent of addressing growing concerns regarding online privacy, Immaculacy is an interactive story that immerses the player in a slightly dystopian world littered with privacy issues. Events unfold in the narrative based on hidden scores kept during gameplay and calculated based on specific decisions made by the player. Ultimately, we hope to create an engaging environment that helps players consider the decisions they are making in their own lives. We give the player experience with many privacy issues through their explorations of a world of hyper surveillance and connectivity. We consider in this chapter a class of two-player nonzero-sum stochastic games with incomplete information, which is inspired by recent applications of game theory in network security. We develop fully distributed reinforcement learning algorithms, which require for each player a minimal amount of information regarding the other player. At each time, each player can be in an active mode or in a sleep mode. If †This material is based upon work supported in part by the U.S. Air Force Office of Scientific Research (AFOSR) under grant number AFOSR MURI FA9550-09-1-0249. D R A F T October 2, 2011, 1:59am D R A F T 2 HYBRID LEARNING IN STOCHASTIC GAMES AND ITS APPLICATION IN NETWORK SECURITY a player is in an active mode, she updates her strategy and estimates of unknown quantities using a specific pure or hybrid learning pattern. The players’ intelligence and rationality are captured by the weighted linear combination of different learning patterns. We use stochastic approximation techniques to show that, under appropriate conditions, the pure or hybrid learning schemes with random updates can be studied using their deterministic ordinary differential equation (ODE) counterparts. Convergence to state-independent equilibria is analyzed for special classes of games, namely, games with two actions, and potential games. Results are applied to network security games between an intruder and an administrator, where the noncooperative behaviors are well characterized by the features of distributed hybrid learning. In this paper, we describe a method to represent and discover adversarial group behavior in a continuous domain. In comparison to other types of behavior, adversarial behavior is heavily structured as the location of a player (or agent) is dependent both on their teammates and adversaries, in addition to the tactics or strategies of the team. We present a method which can exploit this relationship through the use of a spatiotemporal basis model. As players constantly change roles during a match, we show that employing a "role-based" representation instead of one based on player "identity" can best exploit the playing structure. As vision-based systems currently do not provide perfect detection tracking (e.g. missed or false detections), we show that our compact representation can effectively "denoise" erroneous detections as well as enabling temporal analysis, which was previously prohibitive due to the dimensionality of the signal. To evaluate our approach, we used a fully instrumented field-hockey pitch with 8 fixed high-definition (HD) cameras and evaluated our approach on approximately 200,000 frames of data from a state-of-the-art real-time player detector and compare it to manually labelled data. With the proliferation of communication networks and mobile devices, the privacy and security concerns on their information flow are raised. Given a critical system that may leak confidential information, the problem consists of verifying and also enforcing opacity by designing supervisors, to conceal confidential information from unauthorized persons. To find out what the intruder sees, it is required to construct an observer of the system. In this paper, we consider incremental observer generation of modular systems, for verification and enforcement of current state opacity. The synchronization of the subsystems generate a large state space. Moreover, the observer generation with exponential complexity adds even larger state space. To tackle the complexity problem, we prove that observer generation can be done locally before synchronizing the subsystems. The incremental local observer generation along with an abstraction method lead to a significant state space reduction compared to traditional monolithic methods. The existence of shared unobservable events is also considered in the incremental approach. Moreover, we present an illustrative example, where the results of verification and enforcement of current state opacity are shown on a modular multiple floor elevator building with an intruder. Furthermore, we extend the current state opacity, current state anonymity, and language based opacity formulations for verification of modular systems.