MockDroid

Beresford, Alastair R.; Rice, Andrew; Skehin, Nicholas; Sohan, Ripduman

doi:10.1145/2184489.2184500

Cited by 286 publications

(27 citation statements)

References 1 publication

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“…Although their scheme can ensure data security, data utility is decreased. The existing location data privacy protection methods [4,32] are mainly classified to three categories: the heuristic privacy-measure methods, the probability-based privacy inference methods and the privacy information retrieval methods. The heuristic privacy-measure methods are mainly to provide the privacy protection measure for some no-high required users, such as k-anonymity [19], t-closing [3], m-invariability [27] and l-diversity [25].…”

Section: Related Workmentioning

confidence: 99%

Location Data Record Privacy Protection based on Differential Privacy Mechanism

Gu¹,

Yang²,

Yin³

2018

ITC

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Now many location data applications have facilitated people's daily life. However, publishing location data may divulge individual sensitive information. Currently many existing privacy protection schemes cannot provide the balance of utility and protection. Furthermore, as the records about location data may be discrete in database, some existing privacy protection schemes are difficult to protect location data information in data mining. In this paper, our works mainly focus on providing a framework for the privacy protection of location data mining. We propose a location data record privacy protection scheme based on differential privacy mechanism, which employs the structure of multi-level query tree to query and publish location data on database. Our proposed location data privacy protection scheme may discover the relationship of location data from database and protect location data mined. As accessing location preference of user may be related to private (sensitive) location, it is very important to protect highly frequent accessing location data when location data are mined. So, our proposed scheme provides a mechanism to protect highly frequent accessing location data (or accessing location preference of user) by distorting accessing frequencies. In the proposed scheme, we first construct the structure of multi-level query tree from database, then we make double processes of selecting data according to accessing frequencies by the exponential mechanism and one process of adding noises to accessing frequencies by the Laplace's mechanism on the multi-level query tree. Compared with the other schemes, the experiments show the data availability of the proposed scheme is higher and the privacy protection of the scheme is effective.

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Section: Related Workmentioning

confidence: 99%

Location Data Record Privacy Protection based on Differential Privacy Mechanism

Gu¹,

Yang²,

Yin³

2018

ITC

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“…Another approach to extending the Android system involves allowing users to formulate policies for the access control of each app [67]. MockDroid is the most well-known system exemplifying this approach.…”

Section: Enhancing System Securitymentioning

confidence: 99%

Protecting contacts against privacy leaks in smartphones

Cha

Pak

2018

PLoS ONE

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Due to recent developments in technologies associated with the Internet of Things (IoT), a large number of people now regularly use smart devices, such as smartwatches and smartphones. However, these devices are prone to data leaks because of security vulnerabilities. In particular, Android devices use permission-based security, which allows users to directly approve permissions requested by an app when installing it. As a result, many malicious apps can obtain and leak private user data by requesting more permissions than are needed. However, it is difficult to identify malicious apps based solely on the requested permissions. A system is hence needed to accurately identify malicious apps and protect private data from them. In this paper, we propose a system for hiding data related to a user’s contacts or providing virtual data according to preconfigured policies when an Android app requests access to them. By hiding data related to the contacts, the proposed system can protect them from malicious apps. By using virtual data, it can even detect malicious apps that leak private data. The system requires less storage and provides faster access to user contacts than prevalent solutions to similar problems.

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“…The platform they propose requires the device to be rooted and allows the user to choose between three different settings where settings can be changed at a later time dynamically. Beresford et al [28] proposed MockDroid where a user could mock an application's access to a resource and allow the user to determine if the application could function without having access to a particular resource. They rely on the idea that an application has to be resilient enough to where if access to a resource is not available, that the application could still function.…”

Section: Related Workmentioning

confidence: 99%

SPE: Security and Privacy Enhancement Framework for Mobile Devices

Krupp

Sridhar

Zhao

2017

IEEE Trans. Dependable and Secure Comput.

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In this paper, we present a security and privacy enhancement (SPE) framework for unmodified mobile operating systems. SPE introduces a new layer between the application and the operating system and does not require a device be jailbroken or utilize a custom operating system. We utilize an existing ontology designed for enforcing security and privacy policies on mobile devices to build a policy that is customizable. Based on this policy, SPE provides enhancements to native controls that currently exist on the platform for privacy and security sensitive components. SPE allows access to these components in a way that allows the framework to ensure the application is truthful in its declared intent and ensure that the user's policy is enforced. In our evaluation we verify the correctness of the framework and the computing impact on the device. Additionally, we discovered security and privacy issues in several open source applications by utilizing the SPE Framework. From our findings, if SPE is adopted by mobile operating systems producers, it would provide consumers and businesses the additional privacy and security controls they demand and allow users to be more aware of security and privacy issues with applications on their devices.

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MockDroid

Cited by 286 publications

References 1 publication

Location Data Record Privacy Protection based on Differential Privacy Mechanism

Location Data Record Privacy Protection based on Differential Privacy Mechanism

Protecting contacts against privacy leaks in smartphones

SPE: Security and Privacy Enhancement Framework for Mobile Devices

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