Biblio

Privacy protection is a vital part of information security. However, the excessive collections and uses of personal information have intensified in the area of mobile apps (applications). To comprehend the current situation of APP personal information security problem of APP, this paper uses a combined approach of static analysis technology, dynamic analysis technology, and manual review to detect and analyze the installed file of mobile apps. 40 mobile apps are detected as experimental samples. The results demonstrate that this combined approach can effectively detect various issues of personal information security problem in mobile apps. Statistics analysis of the experimental results demonstrate that mobile apps have outstanding problems in some aspects of personal information security such as privacy policy, permission application, information collection, data storage, etc.

People continue to store their sensitive information in their smart-phone applications. Users seldom read an app's privacy policy to see how their information is being collected, used, and shared. In this paper, using a reference list of over 600 Personally Identifiable Information (PII) attributes, we investigate the privacy policies of 100 popular health and fitness mobile applications in both Android and iOS app markets to find the set of personal information these apps collect, use and share. The reference list of PII was independently built from a longitudinal study at The University of Texas investigating thousands of identity theft and fraud cases where PII attributes and associated value and risks were empirically quantified. This research leverages the reference PII list to identify and analyze the value of personal information collected by the mobile apps and the risk of disclosing this information. We found that the set of PII collected by these mobile apps covers 35% of the entire reference set of PII and, due to dependencies between PII attributes, these mobile apps have a likelihood of indirectly impacting 70% of the reference PII if breached. For a specific app, we discovered the monetary loss could reach \$1M if the set of sensitive data it collects is breached. We finally utilize Bayesian inference to measure risks of a set of PII gathered by apps: the probability that fraudsters can discover, impersonate and cause harm to the user by misusing only the PII the mobile apps collected.

The prevalence of photo processing apps suggests the demands of picture editing. As an implementation of the convolutional neural network, style transfer has been deep investigated and there are supported materials to realize it on PC platform. However, few approaches are mentioned to deploy a style transfer model on the mobile and meet the requirements of mobile users. The traditional style transfer model takes hours to proceed, therefore, based on a Perceptual Losses algorithm [1], we created a feedforward neural network for each style and the proceeding time was reduced to a few seconds. The training data were generated from a pre-trained convolutional neural network model, VGG-19. The algorithm took thousandth time and generated similar output as the original. Furthermore, we optimized the model and deployed the model with TensorFlow Mobile library. We froze the model and adopted a bitmap to scale the inputs to 720×720 and reverted back to the original resolution. The reverting process may create some blur but it can be regarded as a feature of art. The generated images have reliable quality and the waiting time is independent of the content and pattern of input images. The main factor that influences the proceeding time is the input resolution. The average waiting time of our model on the mobile phone, HUAWEI P20 Pro, is less than 2 seconds for 720p images and around 2.8 seconds for 1080p images, which are ten times slower than that on the PC GPU, Tesla T40. The performance difference depends on the architecture of the model.

Mobile expressive rendering gained increasing popularity among users seeking casual creativity by image stylization and supports the development of mobile artists as a new user group. In particular, neural style transfer has advanced as a core technology to emulate characteristics of manifold artistic styles. However, when it comes to creative expression, the technology still faces inherent limitations in providing low-level controls for localized image stylization. This work enhances state-of-the-art neural style transfer techniques by a generalized user interface with interactive tools to facilitate a creative and localized editing process. Thereby, we first propose a problem characterization representing trade-offs between visual quality, run-time performance, and user control. We then present MaeSTrO, a mobile app for orchestration of neural style transfer techniques using iterative, multi-style generative and adaptive neural networks that can be locally controlled by on-screen painting metaphors. At this, first user tests indicate different levels of satisfaction for the implemented techniques and interaction design.

In this paper, we propose the first traffic monitoring and labeling system for iOS devices, named iMonitor, which not just captures mobile network traffic in .pcap files, but also provides comprehensive APP-related and user-related information of captured packets. Through further analysis, one can obtain the exact APP or device where each packet comes from. The labeled traffic can be used in many research areas for mobile security, such as privacy leakage detection and user profiling. Given the implementation methodology of NetworkExtension framework of iOS 9+, APP labels of iMonitor are reliable enough so that labeled traffic can be regarded as training data for any traffic classification methods. Evaluations on real iPhones demonstrate that iMonitor has no notable impact upon user experience even with slight packet latency. Also, the experiment result supports our motivation that mobile traffic monitoring for iOS is absolutely necessary, as traffic generated by different OSes like Android and iOS are different and unreplaceable in researches.

Training deep neural networks is a computationally expensive task. Furthermore, models are often derived from proprietary datasets that have been carefully prepared and labelled. Hence, creators of deep learning models want to protect their models against intellectual property theft. However, this is not always possible, since the model may, e.g., be embedded in a mobile app for fast response times. As a countermeasure watermarks for deep neural networks have been developed that embed secret information into the model. This information can later be retrieved by the creator to prove ownership. Uchida et al. proposed the first such watermarking method. The advantage of their scheme is that it does not compromise the accuracy of the model prediction. However, in this paper we show that their technique modifies the statistical distribution of the model. Using this modification we can not only detect the presence of a watermark, but even derive its embedding length and use this information to remove the watermark by overwriting it. We show analytically that our detection algorithm follows consequentially from their embedding algorithm and propose a possible countermeasure. Our findings shall help to refine the definition of undetectability of watermarks for deep neural networks.

In this paper, we present the design of Intelligent Security Lock prototype which acts as a smart electronic/digital door locking system. The design of lock device and software system including app is discussed. The paper presents idea to control the lock using mobile app via Bluetooth. The lock satisfies comprehensive security requirements using state of the art technologies. It provides strong authentication using face recognition on app. It stores records of all lock/unlock operations with date and time. It also provides intrusion detection notification and real time camera surveillance on app. Hence, the lock is a unique combination of various aforementioned security features providing absolute solution to problem of security.

With the popularity of cloud computing, database outsourcing has been adopted by many companies. However, database owners may not 100% trust their database service providers. As a result, database privacy becomes a key issue for protecting data from the database service providers. Many researches have been conducted to address this issue, but few of them considered the simultaneous transparent support of existing DBMSs (Database Management Systems), applications and RADTs (Rapid Application Development Tools). A transparent framework based on accessing bridge and mobile app for protecting database privacy with PKI (Public Key Infrastructure) is, therefore, proposed to fill the blank. The framework uses PKI as its security base and encrypts sensitive data with data owners' public keys to protect data privacy. Mobile app is used to control private key and decrypt data, so that accessing sensitive data is completely controlled by data owners in a secure and independent channel. Accessing bridge utilizes database accessing middleware standard to transparently support existing DBMSs, applications and RADTs. This paper presents the framework, analyzes its transparency and security, and evaluates its performance via experiments.