A Context-Perceptual Privacy Protection Approach on Android Devices

Do My Emotions Influence What I Share? Analysing the Effects of Emotions on Privacy Leakage in Twitter

The privacy leakage resulting from location-based service (LBS) has become a critical issue. To preserve user privacy, many previous studies have investigated to prevent LBS servers from user privacy theft. However, they only consider whether the peers are innocent or malicious but ignore the relationship between the peers, whereas such a relationship between each pairwise of users affects the privacy leakage tremendously. For instance, a user has less concern of privacy leakage from a social friend than a stranger. In this paper, we study cyber-physical-social (CPS) aware method to address the privacy preserving in the case that not only LBS servers but also every other participant in the network has the probability to be malicious. Furthermore, by exploring the physical coupling and social ties among users, we construct CPS-aware privacy utility maximization (CPUM) game. We then study the potential Nash equilibrium of the game and show the existence of Nash equilibrium of CPUM game. Finally, we design a CPS-aware algorithm to find the Nash equilibrium for the maximization of privacy utility. Extensive evaluation results show that the proposed approach reduces privacy leakage by 50% in the case that malicious servers and users exist in the network.

What is it that makes an app malicious? One important factor is that malicious apps treat sensitive data differently from benign apps. To capture such differences, we mined 2,866 benign Android applications for their data flow from sensitive sources, and compare these flows against those found in malicious apps. We find that (a) for every sensitive source, the data ends up in a small number of typical sinks; (b) these sinks differ considerably between benign and malicious apps; (c) these differences can be used to flag malicious apps due to their abnormal data flow; and (d) malicious apps can be identified by their abnormal data flow alone, without requiring known malware samples. In our evaluation, our mudflow prototype correctly identified 86.4% of all novel malware, and 90.1% of novel malware leaking sensitive data.

Android phones often carry personal information, attracting malicious developers to embed code in Android applications to steal sensitive data. With known techniques in the literature, one may easily determine if sensitive data is being transmitted out of an Android phone. However, transmission of sensitive data in itself does not necessarily indicate privacy leakage; a better indicator may be whether the transmission is by user intention or not. When transmission is not intended by the user, it is more likely a privacy leakage. The problem is how to determine if transmission is user intended. As a first solution in this space, we present a new analysis framework called AppIntent. For each data transmission, AppIntent can efficiently provide a sequence of GUI manipulations corresponding to the sequence of events that lead to the data transmission, thus helping an analyst to determine if the data transmission is user intended or not. The basic idea is to use symbolic execution to generate the aforementioned event sequence, but straightforward symbolic execution proves to be too time-consuming to be practical. A major innovation in AppIntent is to leverage the unique Android execution model to reduce the search space without sacrificing code coverage. We also present an evaluation of AppIntent with a set of 750 malicious apps, as well as 1,000 top free apps from Google Play. The results show that AppIntent can effectively help separate the apps that truly leak user privacy from those that do not.

The widespread use of Internet of Things (IoT) technology has promoted location‐based service (LBS) applications. Users can enjoy various conveniences brought by LBS by providing location information to LBS. However, it also brings potential privacy threats to location information. Location data that contains private information is often transmitted among IoT networks in LBS, and such privacy information should be protected. In order to solve the problem of location privacy leakage in LBS, a location privacy protection scheme based on k‐anonymity is proposed in this paper, in which the Geohash coding model and Voronoi graph are used as grid division principles. We adopt the client‐server‐to‐user (CS2U) model to protect the user’s location data on the client side and the server side, respectively. On the client side, the Geohash algorithm is proposed, which converts the user’s location coordinates into a Geohash code of the corresponding length. On the server side, the Geohash code generated by the user is inserted into the prefix tree, the prefix tree is used to find the nearest neighbors according to the characteristics of the coded similar prefixes, and the Voronoi diagram is used to divide the area units to complete the pruning. Then, using the Geohash coding model and the Voronoi diagram grid division principle, the G‐V anonymity algorithm is proposed to find k neighbors in an anonymous area so that the user’s location data meets the k‐anonymity requirement in the area unit, thereby achieving anonymity protection of location privacy. Theoretical analysis and experimental results show that our method is effective in terms of privacy and data quality while reducing the time of data anonymity.

Social media has become an integral part of modernday society. With increasingly digital societies, individuals have become more familiar and comfortable in using Online Social Networks (OSNs) for just about every aspect of their lives. This higher level of comfort leads to users spilling their emotions on OSNs and eventually their private information. In this work, we aim to investigate the relationship between users' emotions and private information in their tweets. Our research question is whether users' emotions, expressed in their tweets, affect their likelihood to reveal their own private information (privacy leakage) in subsequent tweets. In contrast to existing survey-based approaches, we use an inductive, data-driven approach to answer our research question. We use state-of-the-art techniques to classify users' emotions, and privacy scoring and employ a new technique involving BERT for binary detection of sensitive data. We use two parallel classification frameworks: one that takes the user's emotional state into account and the other for the detection of sensitive data in tweets. Consecutively, we identify individual cases of correlation between the two. We bring the two classifiers together to interpret the changes in both factors over time during a conversation between individuals. Variations were found with respect to the kinds of private information revealed in different states. Our results show that being in negative emotional states, such as sadness, anger or fear, leads to higher privacy leakage than otherwise.

With the wide application of machine learning technology, more and more sensitive data were used to develop the machine learning model. Unfortunately, machine learning systems have been shown to be vulnerable to various privacy leakage attacks, such as membership inference attacks and model inversion attacks. These privacy threats break down the confidentiality of the training data; what’s more, the leakage of sensitive privacy will reduce the enthusiasm of data owners for sharing data with the machine learning system. Without enough training data, more seriously, it will hinder the application of machine learning. Therefore, it requires analyzing strategies for privacy risks to help data owners pre-assess the candidate dataset and assist data owners in implementing reasonable privacy control. However, the systematic privacy risk assessment is still absent from the data owner’s perspective.This paper investigates and analyzes machine learning privacy risks to understand the relationship between training data properties and privacy leakage. Based on this recognition, we introduce a privacy risk assessment scheme based on the clustering distance of training data. Our clustering distance-based method can reflect the privacy risk level of a different individual data record. And then, we combine both existing privacy analysis based on data features and our clustering distance-based method to investigate privacy risks systematically. Our experiments showed that our clustering distance method and other set properties are tightly related to privacy leakage. And data owners can pre-assess the privacy risks of candidate datasets before uploading or sharing them to the machine learning model. If needed, decide to re-choose the dataset to reduce privacy risks to an acceptable level.

Mobile devices with Global Positioning System enable various kinds of location information searches using location based services (LBS). Location information and query contents of searchers are privacy information. Privacy protection requires to hide location information and query contents of searchers from not only third parties such as eavesdroppers but also location database servers which answer queries. This paper focuses on privacy protection in nearest neighbor search. There are several researches of secure computation in nearest neighbor search for other than LBS. This paper proposes a new method of privacy protection in LBS. The method is based on Asymmetric Scalar-product Preserving Encryption. This paper also shows that the proposed method is faster and more secure than conventional privacy protection methods in LBS.

Managing privacy leakage is of great importance in the Android platform. The variety of new user-privacy fraud reveals a new challenge in predicting potential privacy disclosure threats and protecting the privacy inside our pocket. In this paper, we present an analysis framework, called AppLeak, for information loss evaluation, privacy leakage detection, and privacy risk assessment on Android applications. With newly formalized privacy measures, AppLeak can effectively and efficiently support mobile user in identifying privacy risks of specific mobile applications.

Android is the most popular mobile platform today, and it is also the mobile operating system that is most heavily targeted by malware. Existing static analyses are effective in detecting the presence of most malicious code and unwanted information flows. However, certain types of malice are very difficult to capture explicitly by modeling permission sets, suspicious API calls, or unwanted information flows. One important type of such malice is malicious application logic, where a program (often subtly) modifies its outputs or performs actions that violate the expectations of the user. Malicious application logic is very hard to identify without a specification of the normal, expected functionality of the application. We refer to malicious application logic that is executed, or triggered, only under certain (often narrow) circumstances as a logic bomb. This is a powerful mechanism that is commonly employed by targeted malware, often used as part of APTs and state-sponsored attacks: in fact, in this scenario, the malware is designed to target specific victims and to only activate under certain circumstances. In this paper, we make a first step towards detecting logic bombs. In particular, we propose trigger analysis, a new static analysis technique that seeks to automatically identify triggers in Android applications. Our analysis combines symbolic execution, path predicate reconstruction and minimization, and inter-procedural control-dependency analysis to enable the precise detection and characterization of triggers, and it overcomes several limitations of existing approaches. We implemented a prototype of our analysis, called TriggerScope, and we evaluated it over a large corpus of 9,582 benign apps from the Google Play Store and a set of trigger-based malware, including the recently-discovered HackingTeam's RCSAndroid advanced malware. Our system is capable of automatically identify several interesting time-, location-, and SMS-related triggers, is affected by a low false positive rate (0.38%), and it achieves 100% detection rate on the malware set. We also show how existing approaches, specifically when tasked to detect logic bombs, are affected by either a very high false positive rate or false negative rate. Finally, we discuss the logic bombs identified by our analysis, including two previously-unknown backdoors in benign apps.

Federated Learning (FL) in Consumer Internet of Things (CIoT) encounters significant privacy security threats when collaborative training Machine Learning models using data distributed among numerous smart CIoT devices. This paper’s objective is to evaluate the potential privacy risks associated with FL within CIoT context, specifically through the lens of passive malicious users. We propose an evaluation scheme (MIGAN) for privacy leakage, which incorporates various privacy inference attacks. First, this paper analyzes the characteristics of privacy inference attacks in FL and summarizes privacy security threats to guide the design of the MIGAN evaluation scheme. Then, to fully utilize the privacy information in FL, this paper proposes faster model inversion and white-box membership inference. Integrating these two modules into the MIGAN framework facilitates fast and high-quality privacy data reconstruction. Finally, this paper constructs the evaluation function to measure the correlation between compromised privacy data and users’ training data. Experiments conducted on the MNIST and CelebA datasets reveal that MIGAN’s data privacy reconstruction outperforms comparable methods. Notably, MIGAN quantifies the likelihood of potential privacy leakage at 14.7% for MNIST and 8.3% for CelebA in FL with 30 clients. These results validates the practicality and relevance of MIGAN in evaluating privacy leakage risks.

Wide spread of electronic imaging equipment and Internet makes photo sharing a popular and influential activity. However, privacy leaks caused by photo sharing have raised serious concerns, and effective methods on privacy protection are required. In this paper, a new privacy-preserving algorithm for photo sharing, especially for human faces in group photos, is proposed based on sparse representation and data hiding. Our algorithm uses a cartoon image to mask the region that contains privacy information, and performs sparse representation to find a more concise expression for this region. Furthermore, the sparse coefficients and part of the residual errors obtained from sparse representation are encoded and embedded into the photo by means of data hiding with a secret key, which avoids introducing extra storage overhead. In this way, the privacy-protected photo is obtained, and only people with correct key can reverse it to the original version. Experimental results demonstrate that the proposed privacy-preserving algorithm does not increase storage or bandwidth requirements, and meanwhile ensures a good quality of the reconstructed image.

SummaryThe ever increasing number of Android malware has always been a concern for cybersecurity professionals. Even though plenty of anti‐malware solutions exist, we hypothesize that the performance of existing approaches can be improved by deriving relevant attributes through effective feature selection methods. In this paper, we propose a novel two‐step feature selection approach based on Rough Set and Statistical Test named as RSST to extract refined system calls, which can effectively discriminate malware from benign apps. By refined set of system call, we mean the existence of highly relevant calls that are uniformly distributed thought target classes. Moreover, an optimal attribute set is created, which is devoid of redundant system calls. To address the problem of higher dimensional attribute set, we derived suboptimal system call space by applying the proposed feature selection method to maximize the separability between malware and benign samples. Comprehensive experiments conducted on three datasets resulted in an accuracy of 99.9%, Area Under Curve (AUC) of 1.0, with 1% False Positive Rate (FPR). However, other feature selectors (Information Gain, CFsSubsetEval, ChiSquare, FreqSel, and Symmetric Uncertainty) used in the domain of malware analysis resulted in the accuracy of 95.5% with 8.5% FPR. Moreover, the empirical analysis of RSST derived system calls outperformed other attributes such as permissions, opcodes, API, methods, call graphs, Droidbox attributes, and network traces.

This paper proposes a concept of serving spatial location information over IP networks. It clarifies and explores the requirements and solutions to realize the concept. The crucial issues elaborated with solutions are: definitions of target and its location, its naming requirements and mechanisms, its roaming among location servers, acquiring spatial location information over IP networks. Recently, issues of location-based applications have been raised in the IP community. This paper contributes to a cutting edge effort for supporting such applications over IP networks.

The usage of Location-Based Services (LBS) holds a potential privacy issue when people exchange their locations for information relative to these locations. While most people perceive these information exchange services as useful, others do not, because an adversary might take advantage of the users' sensitive data. In this paper, we propose k-path, an algorithm for privacy protection for continuous location tracking-typed LBS. We take inspiration in k-anonymity to hide the user location or trajectory among k locations or trajectories. We introduce our simulator as a tool to test several strategies to hide users' locations. Afterwards, this paper will give an evaluation about the effectiveness of several approaches by using the simulator and data provided by the GeoLife data set.