Leakage Detection and Risk Assessment on Privacy for Android Applications: LRPdroid

Lo, Nai-Wei; Yeh, Kuo-Hui; Fan, Chuan-Yen

doi:10.1109/jsyst.2014.2364202

Cited by 9 publications

(11 citation statements)

References 20 publications

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“…The features that are compared between these models are attributes, assessment, environment, and multi/single faceted. These previous works [7,8,10,11] are compared because they have a similarity with the present study. From the existing works, this study found significant elements that have been overlooked in their models.…”

Section: Existing Privacy Models and Featuresmentioning

confidence: 87%

“…A second reason for quantifying privacy risk using a multifaceted model is to expose the amount of user data collected by each application. This element is compulsory in quantifying privacy risk because normal users do not realize how much data are being accessed and collected by the applications based on their usage behaviour [ 7 , 8 ]. No existing work that has done a privacy model by considering user data size.…”

Section: Introductionmentioning

confidence: 99%

“…The focus of this article is on answering the three research questions mentioned. The techniques used in constructing the model produce results that are competitive with existing studies such as LRPdroid [ 7 ], PUREdroid [ 8 ], Online Social Network privacy score [ 10 ], and Identity Theft Assessment and Prediction (ITAP) [ 11 ]. The contributions are as follows: A novel mathematical model to quantify the privacy and risk of the user in an Android smartphone environment is developed.…”

Section: Introductionmentioning

confidence: 99%

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A Privacy Preservation Quality of Service (QoS) Model for Data Exposure in Android Smartphone Usage

Bakar

Singh

Shariff

2021

Sensors

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An Android smartphone contains built-in and externally downloaded applications that are used for entertainment, finance, navigation, communication, health and fitness, and so on. The behaviour of granting permissions requested by apps might expose the Android smartphone user to privacy risks. The existing works lack a formalized mathematical model that can quantify user and system applications risks. No multifaceted data collector tool can also be used to monitor the collection of user data and the risk posed by each application. A benchmark of the risk level that alerts the user and distinguishes between acceptable and unacceptable risk levels in Android smartphone user does not exist. Hence, to address privacy risk, a formalized privacy model called PRiMo that uses a tree structure and calculus knowledge is proposed. An App-sensor Mobile Data Collector (AMoDaC) is developed and implemented in real life to analyse user data accessed by mobile applications through the permissions granted and the risks involved. A benchmark is proposed by comparing the proposed PRiMo outcome with the existing available testing metrics. The results show that Tools & Utility/Productivity applications posed the highest risk as compared to other categories of applications. Furthermore, 29 users faced low and acceptable risk, while two users faced medium risk. According to the benchmark proposed, users who faced risks below 25% are considered as safe. The effectiveness and accuracy of the proposed work is 96.8%.

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Section: Existing Privacy Models and Featuresmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Privacy Preservation Quality of Service (QoS) Model for Data Exposure in Android Smartphone Usage

Bakar

Singh

Shariff

2021

Sensors

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“…At the same time, the users should be provided with the authority to delete or change certain personal details that they have provided to the app database at the first place. These determine to what extent the developer is committed to respect and protect the privacy of the users and how they plan to protect their best interest (Lo, Yeh & Fan, 2016).…”

Section: The Importance Of Governance In Risk Management In Applicationsmentioning

confidence: 99%

“…al., 2015). This is why governance of risk management needs to focus on two types of security measures, one for ensuring that the security layers are not being beached by any element and secondly to make sure that even if there is a leakage, that is being understood by the admin within a reasonable period of time so that the problem can be taken care of (Lo, Yeh & Fan, 2016).…”

Section: The Importance Of Governance In Risk Management In Applicationsmentioning

confidence: 99%

Risk Management Governance in Applications

2020

IKM

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This paper is an overview of risk management governance in applications, a detailed discussion has been provided regarding the importance of developing and implementing a well-organized risk management governance that will enhance the trust of the users when it comes to using an application and at the same time will also provide them with a safety net which will be designed to protect them from any type of security breach. The paper will also stress on the necessity of the application developers to remain proactive to identify future potential threats that may overpower the existing security system and prepare them accordingly. In case a data leakage is in place, there should be a proper mechanism to identify the leakage and amend it within a short period of time which will work as a damage control initiative. This paper will also discuss certain aspects that are closely related to gaining the trust of the users which may include a proper safety rating system that can be interpreted by the users. Secondly, there is a need to ensure that the apps are demanding for less permission which may assist with reducing the possibility of serious privacy violation in times of data breach.

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