2,195 research outputs found
An Empirical Study on Android for Saving Non-shared Data on Public Storage
With millions of apps that can be downloaded from official or third-party
market, Android has become one of the most popular mobile platforms today.
These apps help people in all kinds of ways and thus have access to lots of
user's data that in general fall into three categories: sensitive data, data to
be shared with other apps, and non-sensitive data not to be shared with others.
For the first and second type of data, Android has provided very good storage
models: an app's private sensitive data are saved to its private folder that
can only be access by the app itself, and the data to be shared are saved to
public storage (either the external SD card or the emulated SD card area on
internal FLASH memory). But for the last type, i.e., an app's non-sensitive and
non-shared data, there is a big problem in Android's current storage model
which essentially encourages an app to save its non-sensitive data to shared
public storage that can be accessed by other apps. At first glance, it seems no
problem to do so, as those data are non-sensitive after all, but it implicitly
assumes that app developers could correctly identify all sensitive data and
prevent all possible information leakage from private-but-non-sensitive data.
In this paper, we will demonstrate that this is an invalid assumption with a
thorough survey on information leaks of those apps that had followed Android's
recommended storage model for non-sensitive data. Our studies showed that
highly sensitive information from billions of users can be easily hacked by
exploiting the mentioned problematic storage model. Although our empirical
studies are based on a limited set of apps, the identified problems are never
isolated or accidental bugs of those apps being investigated. On the contrary,
the problem is rooted from the vulnerable storage model recommended by Android.
To mitigate the threat, we also propose a defense framework
Vulnerable GPU Memory Management: Towards Recovering Raw Data from GPU
In this paper, we present that security threats coming with existing GPU
memory management strategy are overlooked, which opens a back door for
adversaries to freely break the memory isolation: they enable adversaries
without any privilege in a computer to recover the raw memory data left by
previous processes directly. More importantly, such attacks can work on not
only normal multi-user operating systems, but also cloud computing platforms.
To demonstrate the seriousness of such attacks, we recovered original data
directly from GPU memory residues left by exited commodity applications,
including Google Chrome, Adobe Reader, GIMP, Matlab. The results show that,
because of the vulnerable memory management strategy, commodity applications in
our experiments are all affected
N′-(2-HydrÂoxy-5-chloroÂbenzylÂidene)-4-nitroÂbenzohydrazide methanol solvate
The title compound, C14H10ClN3O4·CH4O, was synthesized from the reaction of 5-chloroÂsalicylaldehyde with 4-nitroÂbenzohydrazide in methanol. The Schiff base molÂecule is nearly planar, with a dihedral angle of 9.1 (3)° between the two benzene rings. The methanol solvent molÂecules are linked to the Schiff base molÂecules by N—H⋯O, O—H⋯N and O—H⋯O hydrogen bonds, forming chains running parallel to the a axis
Determinants of patient’s satisfaction and predicting Patient’s willingness to return: a case from a Chinese town hospital
As the amount of hospitals increases drastically in China and the need for high quality medical care keeps rising, awareness has been raised for hospitals to maintain their standards by being aligned with national requirements as well as to continuously improve their service for patients. It has been estimated that nearly 75% of clinical cases are not properly diagnosed, treated or supervised afterwards in many developing countries.
Because different patients have various needs or requests for medical service, it can be extremely tough for hospitals to satisfy every patient. In order to increase satisfaction level, it is essential to measure patients’ satisfaction not only in favor of the overall experience of patients, but also of hospital itself such as better presentation, more patient’s visits and better reputation. Patients’ satisfaction can be referred as patients’ feedback towards various aspects of their subjective dimensions of experience. With the results from patients, hospitals are able to identify which need to be improved, and then make corresponding decisions in pursuit for better services and quality based on patients’ desires.
The purpose of the thesis is to discover the significant determinants influencing patients’ satisfaction and to predict the willingness to return in the case hospital. The case hospital in Shahu central hospital. In this thesis, I will mainly focus on the determinants related to the case hospital and demographical predictors. Regarding determinants concerning the case hospital, they include the level of care, tangibles, price, accessibility and corruption level, which are partially based on SERVQUAL model. Furthermore, the demographical predictors are comprised of gender, age, education level, socio-economic status and health conditions. In order to implement the research, a questionnaire is distributed to patients visiting the case hospital. Results are then analyzed with python using various models: regression analysis, Pearson correlation, decision tree and random forest.
The results indicate that price, tangibles, accessibility, the level of professional care and interpersonal care and patients’ health conditions are of great significant to patients’ satisfaction level in the case hospital. Besides, price is negatively associated with patients’ satisfaction level, while other significant predictors show positive relationship with patients’ satisfaction level
Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild
In this paper, we seek to better understand Android obfuscation and depict a
holistic view of the usage of obfuscation through a large-scale investigation
in the wild. In particular, we focus on four popular obfuscation approaches:
identifier renaming, string encryption, Java reflection, and packing. To obtain
the meaningful statistical results, we designed efficient and lightweight
detection models for each obfuscation technique and applied them to our massive
APK datasets (collected from Google Play, multiple third-party markets, and
malware databases). We have learned several interesting facts from the result.
For example, malware authors use string encryption more frequently, and more
apps on third-party markets than Google Play are packed. We are also interested
in the explanation of each finding. Therefore we carry out in-depth code
analysis on some Android apps after sampling. We believe our study will help
developers select the most suitable obfuscation approach, and in the meantime
help researchers improve code analysis systems in the right direction
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