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...ion and authentication, thus a network attacker can control the robot or extract sensitive data. By contrast, DARKLY protects users from untrusted applications running on a trusted robot. PlaceRaider =-=[25]-=- is a hypothetical mobile malware that can construct a 3-D model of its environment from phonecamera images. DARKLY prevents this and similar attacks. SciFi [21] uses secure multiparty computation to ...

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...sable the camera or to screen photos carefully before sharing them, malware could take and transmit photos surreptitiously; work on visual malware for smartphones has already demonstrated this threat =-=[52]-=-. As firstperson devices become more popular and capture ever greater numbers of photos, people’s privacy will be at even greater risk. At a collection interval of 30 seconds, the Narrative Clip can c...

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...hese devices. Recent work has shown how opportunistic images generate new threats to users, such as allowing 3-D models of their environment to be surreptitiously created and enabling ‘virtual theft’ =-=[28]-=-. Complementary work includes defensive frameworks in which users define policies based on physical location, so that photos taken in predefined sensitive spaces can be recognized and then deleted or ...

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...), or vice versa. Size manipulation attacks, in which the parent application violates the child element’s size requirements or expectations (e.g., to secretly take photos by hiding the camera preview =-=[26]-=-), or the child element sets it own size inappropriately (e.g., to display a full-screen ad). Input forgery attacks, in which the parent application delivers forged user input to a child element (e.g....

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...l-documented shortcomings in existing permission systems have allowed powerful sensor-based attacks that actually give remote hackers ‘virtual access’ to the physical environment surrounding a device =-=[32,35]-=-. One solution is to use fine-grained permissions consistent with the principle of least privilege [9]. Unfortunately, this requires verbose policies or a high degree of user workload to manage access...

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...board and the keyboard is used for typing, then by measuring the relative physical position and distance between the vibration, they could recover words with accuracy as high as 80%. Templeman et al. =-=[27]-=- propose a proof-of-concept visual malware called PlaceRaider. It opportunistically uses camera and other sensory data from a smartphone to create a 3D model of the user’s environment. This 3D model a...

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...ich may contain sensitive business information directly. With some special designs, even credit card and PIN number can be extracted from recorded voice [44]. Nearly all previous sensor based attacks =-=[44, 39, 12, 9, 45, 47, 29]-=- only considered invoking input type of device components to achieve malicious targets, such as accelerometer for device posture analysis and microphone for audio recording. These attacks are based on...

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... effort at analyzing, and improving access control in smartphone frameworks [20, 49, 22, 24, 51, 50, 10, 16, 43, 68]. For instance, Enck et al. introduced the idea that dangerous permission combinations are indicative of possibly malicious activity [20]. Roesner et al. introduced User-Driven Access control where apps prompt for permissions only when they need it [51, 50]. However, permissions are fundamentally only gate-keepers. The PlaceRaider sensory malware abuses granted permissions and uses smartphone sensors (e.g., camera) to reconstruct the 3D environment of the user for reconnaissance [64]. This malware exploits the inability of permission systems to control data usage once access in granted. The IoT fundamentally has a lot more sensitive data than a single smartphone camera, motivating the need for a security model that is capable of strictly controlling data use once apps obtain access. PiBox does offer privacy guarantees using differential-privacy algorithms after apps gain permissions, but it is primarily applicable to apps that gather aggregate statistics [43]. In contrast, FlowFence controls data flows between arbitrary types of publishers and consumers. Label-based Infor...

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...ers in Collecting Mode. Users touch the identical icons. important to filter it out. At present however, PIN Skimmer does not implement this mode but previous research suggests this could be possible =-=[17, 18]-=-. So this mode is left for future research. Collecting Mode: In Collecting Mode, the user interacts with the malware, in the form of a (malicious) game running in the Android OS as illustrated in Fig....

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...ers in Collecting Mode. Users touch the identical icons. important to filter it out. At present however, PIN Skimmer does not implement this mode but previous research suggests this could be possible =-=[17, 18]-=-. So this mode is left for future research. Collecting Mode: In Collecting Mode, the user interacts with the malware, in the form of a (malicious) game running in the Android OS as illustrated in Fig....