Set Of Permissions Research Articles

Bi-objective Optimization in Role Mining

Role mining is a technique that is used to derive a role-based authorization policy from an existing policy. Given a set of users U , a set of permissions P and a user-permission authorization relation UPA ⊆ U × P , a role mining algorithm seeks to compute a set of roles R , a user-role authorization relation UA ⊆ U × R and a permission-role authorization relation PA ⊆ R × P , such that the composition of UA and PA is close (in some appropriate sense) to UPA . Role mining is therefore a core problem in the specification of role-based authorization policies. Role mining is known to be hard in general and exact solutions are often impossible to obtain, so there exists an extensive literature on variants of the role mining problem that seek to find approximate solutions and algorithms that use heuristics to find reasonable solutions efficiently. In this paper, we first introduce the Generalized Noise Role Mining problem (GNRM) – a generalization of the MinNoise Role Mining problem – which we believe has considerable practical relevance. In particular, GNRM can produce “security-aware” or “availability-aware” solutions. Extending work of Fomin et al., we show that GNRM is fixed parameter tractable, with parameter r + k , where r is the number of roles in the solution and k is the number of discrepancies between UPA and the relation defined by the composition of UA and PA . We further introduce a bi-objective optimization variant of GNRM, where we wish to minimize both r and k subject to upper bounds \(r \le \bar{r} \) and \(k\le \bar{k} \) , where \(\bar{r} \) and \(\bar{k} \) are constants. We show that the Pareto front of this bi-objective optimization problem (BO-GNRM) can be computed in fixed-parameter tractable time with parameter \(\bar{r} +\bar{k} \) . From a practical perspective, a solution to BO-GNRM gives security managers the opportunity to identify a mined policy offering the best trade-off between the number of policy discrepancies and the number of roles. We then report the results of our experimental work using the integer programming solver Gurobi to solve instances of BO-GNRM. Our key findings are that (a) we obtained strong support that Gurobi’s performance is fixed-parameter tractable, (b) our results suggest that our techniques may be useful for role mining in practice, based on our experiments in the context of three well-known real-world authorization policies. We observed that, in many cases, our solver is capable of obtaining optimal solutions when the values of either k or r are small.

A-003 Beckman Coulter DxI 9000 Immunoassay Analyzer’s High Sensitivity Troponin I achieves >90% six sigma performance when assessed against clia 2024 performance goals

Abstract Background In July 2024, the Clinical Laboratory Improvement Act (CLIA) proficiency testing (PT) criteria will directly regulate Troponin I performance for the very first time. The new CLIA goal is 0.9 ng/mL or 30%, whichever is greater. CAP previously set a goal of 30% or 3 times the group standard deviation (SD), whichever is greater, a more permissive setting. Estimates of current instrument group performance from an international proficiency testing (PT) survey have shown none of the 5 major diagnostic instruments can achieve the biological minimum goal at a 6-Sigma level, while 4 of the 5 instruments perform at 3 Sigma or below. Performance of these platforms was assessed using the methodology introduced in 2006 by Westgard JO and Westgard SA. The DxI 9000 high sensitivity Troponin I assay was assessed to determine if it could achieve the new CLIA 2024 goals. Methods The DxI 9000 high sensitivity Troponin I assay was assessed with three reagent lots, on both serum and Lithium Heparin (LiHep) samples, following Clinical Laboratory Standards Institute (CLSI) protocols EP05 and EP09 to estimate imprecision and bias. The new CLIA 2024 PT criteria supplied the allowable total error for the standard Sigma-metric calculation: Sigma-metric = (TEa - |bias|) / SD The Sigma-metric predicts not only future problems with PT, but also potential optimization of QC procedures, including fewer Westgard Rules, control levels, even reduced QC frequency which can lead to less cost, time, and materials. Results The majority (91.7%) of data points across the analytical measuring range for DxI 9000’s high sensitivity Troponin I assay from both serum and LiHep samples achieved 6-Sigma performance. For serum, only 8.3% of samples achieved 5-sigma performance. For LiHep, only 4.2% of the performance was 4 Sigma. None of the performance was 3 Sigma or lower. Conclusions The superior precision observed on DxI 9000 high sensitivity Troponin I delivers overwhelming 6-sigma performance when assessed by CLIA’s 2024 goal. This assay is highly unlikely to face PT difficulties and can be optimized for reduced Westgard Rules, reduced control levels leading to a reduction in time, materials, and cost.

Is the Tigray region, Ethiopia also affected by fluoride in drinking water affecting public health?

The purpose of this study was to assess the levels of fluoride in drinking water and its health impact in Semema, Tigray, Ethiopia. Water samples were collected in February, March and April from three potential spring water sources, namely May Atkaru, May Sensela and May Liham. Each sample was analyzed for a variety of physicochemical parameters including fluoride using standard APHA procedures through double beam UV-Visible spectrophotometer, atomic absorption spectrophotometer and titrimetric methods. All the measured physicochemical parameters except hardness (345.78-368.35 mg/L) and alkalinity (231.3-354.6 mg/L) were recorded below the WHO permissible limit set for drinking water. The amount of fluoride in May Atkaru (4.00 mg/L) and May Sensela (3.89 mg/L) was significantly greater than the WHO permissible limit set for drinking water, 1.5 mg/L. Moreover, HQ > 1 from May Atkaru and May Sensela revealed the possibility of dental and skeletal fluorosis over extended exposure to fluoride irrespective of age and sex variations. This confirmed people in the area with mottled teeth are vulnerable to the excessive consumption of fluoride, which poses health risks. Therefore, it needs immediate interventions to minimize the debilitating effect of fluoride in drinking water by creating awareness among the community and policymakers to introduce low-cost defluoridation methods.

Evaluation of groundwater quality for drinking water using a quality index in Abyi Adi, Tigrai, Northern Ethiopia

High quality, safe and sufficient drinking water is essential for public health and well-being. However, the war on Tigrai damaged the water sources of communities and pose people to health problems. Therefore, the aim of the present study was to assess the quality of water of the town of Abyi Adi, Tigrai, Northern Ethiopia using physicochemical and biological parameters and water quality indices. A total of 36 water samples were collected from four major water sources. The physicochemical and biological parameters were determined using standard analytical procedures for water analysis. The mean values of electrical conductivity and pH ranged from 273.63 to 881.27 μS/cm and 6.68 to 7.42, respectively. Moreover, the experimental results of major cations (Na+ = 3.70–14.77 mg/L and Ca2+ = 8.50–15.77 mg/L), and anions (HCO3− = 21.52–40.77 mg/L, Cl− = 13.56–40.29 mg/L, NO3− = 0.14–0.25 mg/L, NO2− = 0.24–0.76 mg/L and PO43− = 0.34–1.32 mg/L) were recorded below the World Health Organization (WHO) permissible limits set for drinking water. The water quality index (WQI) that is determined using a weighted arithmetic water quality index method (WAWQIM) was also found in the range of 5.3–37.2. Subsequently, all groundwater sources except Adibakla are classified as excellent or “A” rating. However, the total coliform of Maylomin and Chiny water sources were found to be 6.33 MPN/100 mL and 3.67 MPN/100 mL, respectively. Both are higher than the WHO permissible limit set for drinking water. Considering the susceptibility of groundwater to pollution and its impact to human health, regular monitoring and supervision should be performed to keep the water safe for drinking. Accordingly, chlorination water treatment process is recommended to provide safe drinking water.

Assessment of seasonal variations of some heavy metals in water samples collected from Gwaigwaye, Maska and Zobe dams

Water pollution can Damage Aquatic Ecosystems, leading to Death of Fish, plants and other Aquatic organism, which in turn can Disrupt the Entire Food chains. This study was conducted to Determine the Concentrations of Some Heavy metals in water samples obtained from Gwaigwaye, Maska and Zobe Dams in Dry and wet Seasons. The Dams provides water for Drinking, Fishing, Farming and other Agricultural Activities to the Neighbouring Communities. In this study water samples collected from Gwaigwaye, Maska and Zobe Dams in Dry and wet Seasons were Analyzed for Some Heavy metals namely, Cadmium, Cobalt, Copper, Nickel, lead and zinc. After Samples Digestion, microwave plasma atomic emission spectroscopy(MPAES) was used for the Determination of the Metal concentrations and their Levels were compared with permissible limits set by local and international standard regulatory Authorities. From the result of the Analysis, the Concentrations of the Heavy metals in Dry season in mg/L ranged as follows. 0.00±0.00 to 0.03±0.00 mg/L for Cd, 0.02±0.00 to 0.084±0.002 mg/L for Co, 0.057±0.003to 0.186±0.003 mg/L for Cu, 0.03±0.00 to 0.19±0.005 mg/L for Ni, 0.00±0.00 to 0.01±0.00 mg/L for Pb and 0.06±0.02-0.193±0.003 mg/L for Zinc. Similarly, the Concentrations of metals in wet season in(mg/L) ranged as follows, 0.00±0.00 to 0.027±0.002 for Cd, 0.02±0.002 to 0.071±0.002 for Co, 0.051±0.003 to 0.103±0.014 for Cu, 0.029±0.003 to 0.12±0.013 for Ni, 0.00±0.00 to 0.00±0.00 for pb and 0.057±0.002 to 0.160±0.003 for Zinc. The Concentrations level of Cd, Co and Ni in water samples from Maska, Gwaigwaye and Zobe Dams in dry season were above the permissible set by WHO/SON. However, Cu, Pb and Zn were within the permissible limit of WHO and SON in all the Dams in dry and wet seasons. The Result of Statistical Analysis indicated no Significant Difference between the Metals in all the Dams in both Dry and Wet seasons as p values were greater than 0.05( p >0.05).

Randomized-controlled study assessing the effect of milking permission settings and concentrate supplementation on milking frequency and milk yield in a pasture-based automatic milking system

This study aimed to verify the impact of milking permission (MP) and concentrate supplementation (CS) on milking frequency (milkings per cow/day) and milk yield (kg per cow/day) in a farm using a pasture-based automatic milking system (AMS). Sixty-eight cows milked using this AMS unit were randomly assigned to one of 4 groups homogeneous for parity, days in milk and milk yield. Treatments used were: Frequent (F) or Restricted (R) MP, that granted cows permission to milk after 6 to 8 h or 9.6 to 14 h of the previous milking, respectively; and low (LC) or high (HC) CS of 0.5 kg or 3.5 kg per cow/day, respectively. The combination of the 2 levels of MP and the 2 levels of CS resulted in the 4 treatment combinations (FHC, RHC, FLC, RLC). This study was designed as a 2 X 2 factorial arrangement with treatment crossover: each of the 4 cow-groups was randomly assigned to one of the 4 treatment combinations for a 5-week experimental period (one pre-treatment week and 4 treatment weeks), and after each 5-week period groups crossed over to another treatment combination until they experienced all. Statistical analysis assessed the impact of MP, CS and their interaction on milk yield, milking frequency, box time, milking time and average milk flow rate. This was done using a mixed model analysis with repeated measures to account for repeated observations on the experimental unit (cow). Milk yield per cow/day and milkings per cow/day were significantly higher with the Frequent compared with the Restricted MP (1.5 kg and 0.65 respectively). Milk yield per cow/day and milkings per cow/day were significantly higher with the HC compared with the LC CS (3.1 kg and 0.25 respectively). Additionally, milk yield per cow/day was affected by the interaction of MP and CS and it was highest with the FHC (20.1 kg) treatment combination, followed by RHC (18.2 kg) treatment combination. The number of milkings per cow/day were also affected by the interaction of MP and CS. The highest estimated number of milkings per cow/day was recorded for the FHC (2.12) and the FLC (1.77) treatment combinations, followed by the RHC (1.38) and RLC (1.23) treatment combinations. Similarly, milking interval was 2.5 h longer for the RLC treatment combination compared with RHC. The shortest milking interval/milking was observed for the FHC (11 h) and FLC (12.8 h) treatment combinations. In conclusion, the study showed that allowing access to the robot between 6 to 8 h after the previous milking was sufficient (even with a minimal level of CS) to achieve acceptable milk production and milking performance in a pasture-based AMS.

If epistemic partialism is true, don’t tell your friends

Abstract It is generally recognized that friendship justifies a particular set of special permissions and responsibilities. However, a compelling debate has emerged regarding the question of whether we ought to believe claims about our friends that strangers would not. Advocates of epistemic partialism, such as Sarah Stroud and Simon Keller, argue that friendship ought to sometimes lead us to form beliefs that are inconsistent with what is justified from an impartial perspective. In this paper, I identify a puzzle for epistemic partialism that has not received attention. The puzzle is that epistemic partialism fails to meet a publicity condition for agents to transparently endorse the doxastic practices the position recommends. If epistemic partialism is widely acknowledged as true, the goods it produces become more difficult to secure. This is not a decisive objection to epistemic partialism, but it may leave the view less convincing than alternatives.

PermDroid a framework developed using proposed feature selection approach and machine learning techniques for Android malware detection

The challenge of developing an Android malware detection framework that can identify malware in real-world apps is difficult for academicians and researchers. The vulnerability lies in the permission model of Android. Therefore, it has attracted the attention of various researchers to develop an Android malware detection model using permission or a set of permissions. Academicians and researchers have used all extracted features in previous studies, resulting in overburdening while creating malware detection models. But, the effectiveness of the machine learning model depends on the relevant features, which help in reducing the value of misclassification errors and have excellent discriminative power. A feature selection framework is proposed in this research paper that helps in selecting the relevant features. In the first stage of the proposed framework, t-test, and univariate logistic regression are implemented on our collected feature data set to classify their capacity for detecting malware. Multivariate linear regression stepwise forward selection and correlation analysis are implemented in the second stage to evaluate the correctness of the features selected in the first stage. Furthermore, the resulting features are used as input in the development of malware detection models using three ensemble methods and a neural network with six different machine-learning algorithms. The developed models’ performance is compared using two performance parameters: F-measure and Accuracy. The experiment is performed by using half a million different Android apps. The empirical findings reveal that malware detection model developed using features selected by implementing proposed feature selection framework achieved higher detection rate as compared to the model developed using all extracted features data set. Further, when compared to previously developed frameworks or methodologies, the experimental results indicates that model developed in this study achieved an accuracy of 98.8%.

Unbounded non-zero inner product encryption

In a non-zero inner product encryption (NIPE) scheme, ciphertexts and keys are associated with vectors from an inner-product space. Decryption of a ciphertext for x→ is allowed by a key for y→ if and only if the inner product 〈x→,y→〉≠0. Existing constructions of NIPE assume the length of the vectors are fixed apriori. We present the first constructions of unbounded non-zero inner product encryption (UNIPE) with constant sized keys. Unbounded here refers to the size of vectors not being pre-fixed during setup. Both constructions, based on bilinear maps, are proven selectively secure under the decisional bilinear Diffie-Hellman (DBDH) assumption.Our constructions are obtained by transforming the unbounded inner product functional encryption (IPFE) schemes of Dufour-Sans and Pointcheval (ACNS 2019), one in the strict domain setting and the other in the permissive domain setting. Interestingly, in the latter case, we prove security from DBDH, a static assumption while the original IPFE scheme relied on an interactive parameterised assumption. In terms of efficiency, features of the IPFE constructions are retained after transformation to NIPE. Notably, the public key and decryption keys have constant size.

On parametric semidefinite programming with unknown boundaries

In this paper, we study parametric semidefinite programs (SDPs) where the solution space of both the primal and dual problems change simultaneously. Given a bounded set, we aim to find the a priori unknown maximal permissible perturbation set within it where the semidefinite program problem has a unique optimum and is analytic with respect to the parameters. Our approach reformulates the parametric SDP as a system of partial differential equations (PDEs) where this maximal analytical permissible set (MAPS) is the set on which the system of PDEs is well-posed. A sweeping Euler scheme is developed to approximate this a priori unknown perturbation set. We prove local and global error bounds for this second-order sweeping Euler scheme and demonstrate the method in comparison to existing SDP solvers and its performance on several two-parameter and three-parameter SDPs for which the MAPS can be visualized.

디지털 적층 제조 장비의 사용자 중심 HMI 디자인 가이드 제안

With the advancement of metal additive manufacturing technology and the digitization of equipment, the need for user-centered design in HMI (Human-Machine Interface) has become increasingly apparent. However, research on design guides and usability test for industrial equipment has been lacking. Therefore, this study applied user-centered design principles to design and manufacture a prototype HMI for metal additive manufacturing equipment, followed by a usability test of the HMI prototype. Based on usability testing and in-depth interview analysis, this study proposes UX/UI design guidelines for user-centered industrial equipment HMI. The usability evaluation highlighted the need to improve the visibility of the system's real-time status, enhance interface intuitiveness for error prevention, and address emergency situations. It is crucial that the interface reflects both the visible and invisible states of the equipment, considering that the operation targets are mechanical equipment but the operating devices are HMI screens. Furthermore, for industrial equipment with numerous complex functions, it is essential to restrict or guide user actions through permission settings. Through the industrial PBF (Powder Bed Fusion) method metal additive manufacturing equipment HMI design guides proposed by this study, we anticipate the development of user-friendly and productivity-enhancing HMI designs.

A Reference Design Model to Manage Consent in Data Subjects-Centered Internet of Things Devices

Internet of Things (IoT) devices have changed how billions of people in the world connect and interact with each other. But, as more people use IoT devices, many questions arise about how these devices handle private data and whether they properly ask for permission when using it. Due to information privacy regulations such as the EU’s General Data Protection Regulation (GDPR), which requires companies to seek permission from data subjects (DS) before using their data, it is crucial for IoT companies to obtain this permission correctly. However, this can be really challenging in the IoT world because people often find it difficult to interact with and manage multiple IoT devices under their control. Also, the rules about privacy are not always clear. As such, this paper proposes a new model to improve how consent is managed in the world of IoT. The model seeks to minimize “consent fatigue” (when people get tired of always being asked for permission) and give DS more control over how their data are shared. This includes having default permission settings, being able to compare similar devices, and, in the future, using AI to give personalized advice. The model allows users to easily review and change their IoT device permissions if previous conditions are not met. It also emphasizes the need for easily understandable privacy rules, clear communication with users, and robust tracking of consent for data usage. By using this model, companies that provide IoT services can do a better job of protecting user privacy and managing DS consent. In addition, companies can more easily comply with data protection laws and build stronger relationships with their customers.

Concentration of Heavy Metals in Traditional and Industrial Fruit Juices from Iran: Probabilistic Risk Assessment Study.

Exposure to heavy metals can endanger the health of exposed people in the long term. The consumption of fruit juice is increasing; it is important to estimate the health risk of consumers due to heavy metals. The current study was carried out for the analysis of toxic metals (lead (Pb), arsenic (As), and cadmium (Cd)) and essential elements (copper (Cu) and zinc (Zn)) in 60 samples of traditional and industrial fruit juices (10 samples of different brands of apple, orange, grape, peach, mango, and pineapple) in Hamadan, West Iran, using inductivity coupled plasma optical emission spectrometry (ICP-OES) method. The validation protocol included precision of the analytical method; recovery, the determination of the limit of detection (LOD), the limit of quantification (LOQ), and linearity were measured. Moreover, risk assessment was detected using target hazard quotient (THQ) and cancer risk (CR) by the Monte Carlo simulation (MCS) model. The ranking of metal concentration in traditional and industrial fruit juices was Zn > Cu > As > Pb > Cd. In all samples, concentrations of heavy metals in industrial fruit juices were higher than traditional fruit juices p < < 0.001. The level of metals in all samples was lower of the US Environmental Protection Agency (USEPA), the World Health Organization (WHO), and the Iran Standard (IS) permissible limit set for drinking water. In terms of non-carcinogenic, values of toxic elements for children and adult in traditional and industrial fruit juices were 1.6E-3 and 1.72E-3 and 2.6E-3 and 1.85E-3, respectively. The 95th percentile of CR in adults and children due to both industrial and traditional fruits juices was higher than 1E-6; hence, reducing the concentration of As in fruit juices should be conducted. Consumption of fruit juice can increase carcinogenic risk of consumers. Therefore, it is recommended to consume it with caution.

Инжиниринг привилегий в задаче построения ролевой политики разграничения доступа

The problem of role engineering is extended by the subproblem of permissions engineering. It is assumed that discretionary access control is specified in the information system. To build a role-based security policy, a technique based on algorithms for formal concept analysis is proposed. Based on the access matrix, a Galois lattice is constructed, the nodes of which are interpreted as possible permissions. Criteria for choosing the optimal set of permissions are determined and a heuristic algorithm for solving the problem is discussed.

Unraveling the Complexity of Mobile Application Permissions: Strategies to Enhance Users’ Privacy Education

Smartphones and other mobile devices have seamlessly integrated into our daily lives offering a multitude of possibilities through various applications. However, this convenience comes at a cost, due to the excessive usage of device permissions claimed by the applications. The management of information privacy in mobile applications presents a formidable challenge for users. For instance, users are confronted with intricate privacy decisions, including the configuration of application permission settings. Unfortunately, many users lack adequate knowledge about how applications utilize their personal data. This research investigates the permissions of seven most popular mobile applications and provides a program to enable the extraction and categorization of permissions. The extracted information, datasets, and insights enrich the foundation of privacy education. The results can be used by educators who can develop workshops that immerse participants in this challenging topic. Therefore, this research paper contributes to the topics of privacy education and privacy awareness.

Nonlinear transformed function-based adaptive finite-time integrated guidance and control design with full state constraints

This paper proposes a novel finite-time integrated guidance and control (IGC) design for skid-to-turn (STT) missiles, taking full state constraints into account. Firstly, the actuator saturation is converted into a state constraint. Secondly, to handle full state constraints effectively, the “non-constrained” IGC (NCIGC) model is constructed by combining the IGC model with a permissible set of states and applying a nonlinear transformed function. Thirdly, a finite-time full state constraints IGC scheme based on the NCIGC model is presented. According to Lyapunov stability theory, the proposed IGC method is verified to ensure that all states of the IGC model converge to a small neighborhood near zero within a finite time while satisfying the predefined state constraints. Finally, numerical simulations are performed to demonstrate the effectiveness and robustness of the proposed IGC scheme.

Detection of Android Malware using Feature Selection with a Hybrid Genetic Algorithm and Simulated Annealing (SVM and DBN)

Because of the widespread use of the Android operating system and the simplicity with which applications can be created on the Android platform, anyone can easily create malware using pre-made tools. Due to the spread of malware among many helpful applications, Android users are experiencing issues. In this study, we showed how to use permissions gleaned from static analysis to identify Android malware. Utilising support vector machines and deep belief networks, we choose the pertinent features from the set of permissions based on this methodology. The suggested technique increases the effectiveness of Android malware detection.

Carbon monoxide formation from total volatile organic compounds from the use of household spray products

Introduction: Air pollutants emitted from household spray products used in our homes and offices have adverse effects on human health. The quantification and identification of emission sources of air pollutants forms a foundation for an effective indoor exposure control. This study presents the results of a fundamental study conducted to evaluate the kinetics and emission trends Total Volatile Organic Compounds (TVOCs) and Carbon Monoxide (CO) from household spray products.&#x0D; Materials and methods: Fortyfive (45) commonly used household spray products were selected for this study. The experiment was conducted in an isolated empty room of dimension (2.72×2.82×2.00) m3 with no known/ significant indoor emission source(s). CO and TVOCs concentrations were measured with Aeroqual® 500 series monitor with CO and TVOCs head at 15 min, 1 h, 3 h, and 24 h, for all 45 samples of household spray products.&#x0D; Results: Spontaneous second – order conversion of TVOCs to CO was observed for most of the spray products in the indoor environment. For the insecticides samples, TVOCs initial concentrations were 7.2–73±19.76 ppm which after one hour the concentrations became 1.8 – 17±7.20 ppm. CO measured initial concentration were 0 – 4±1.08 ppm which the concentration levels reduced to 0–7±2.16 ppm. TVOCs concentration was above the permissible limit set by USEPA and CO concentration for some of the air fresheners, perfume, shoe impregnation spray and hair sprays fall short the limit of 40,081.89 and 25,562.37 µg/m3 set by United States Environmental Protection Agency (USEPA) and World Health Organization (WHO), respectively.&#x0D; Conclusion: As the concentration of TVOCs decreased as the concentration the concentration, CO increased following a second order kinetics. The result obtained will help in the development of safer products and a proper guide on how to use them in a way it will not cause harm to both the user of the product and the environment.

Role mining under User-Distribution cardinality constraint

Role-based access control (RBAC) defines the methods complex organizations use to assign their users permissions for accessing restricted resources. RBAC assigns users to roles, where roles determine the resources each user can access. The definition of roles, especially when there is a large number of users and many resources to handle, can be a very difficult and time consuming task. The class of tools and methodologies to elicit roles starting from existing user-permission assignments are referred to as role mining. Sometimes, to let the RBAC model be directly deployable in organizations, role mining can also take into account various constraints, like cardinality and separation of duty. Typically, these constraints are enforced to ease roles’ management and their use is justified as role administration becomes convenient. In this paper, we focus on the User-Distribution cardinality constraint which places a restriction the number of users that can be assigned to a given role. In this scenario, we present a simple heuristic that improves over the state-of-the-art. Furthermore, to address a more realistic situation, we provide the User-Distribution model with the additional constraint that avoids the generation of roles sharing identical set of permissions. Similarly, within this context, we describe a heuristic enabling the computation of a solution in the new model. Additionally, we assess both heuristics’ performances using real-world datasets.

Enhancing Android Framework Used to Detect Unexpected Permission Authorization of Mobile Application

The use of mobile devices is expanding daily in today's technological age. Mobile marketplaces are constantly providing an expanding selection of mobile applications to satisfy the demands of smartphone users. Many Android applications fall short in their attempts to adequately address security-related issues. This is usually brought on by a lack of automated methods for permission-based vulnerability discovery, testing, and resolution during early design and development phases. As a result, it is generally agreed that addressing such issues quickly is preferable to sending updates and fixes for already-released apps.A proactive set of permissions declared by mobile app developers can protect users’ data privacy is the research concern here. This paper reviews the AndRev-Android framework functionality and its envisioned purpose. The researcher tried to justify the research questions raised by the experts, which will add value to future researchers in this domain.