23,334 research outputs found
A comprehensive meta-analysis of cryptographic security mechanisms for cloud computing
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The concept of cloud computing offers measurable computational or information resources as a service over the Internet. The major motivation behind the cloud setup is economic benefits, because it assures the reduction in expenditure for operational and infrastructural purposes. To transform it into a reality there are some impediments and hurdles which are required to be tackled, most profound of which are security, privacy and reliability issues. As the user data is revealed to the cloud, it departs the protection-sphere of the data owner. However, this brings partly new security and privacy concerns. This work focuses on these issues related to various cloud services and deployment models by spotlighting their major challenges. While the classical cryptography is an ancient discipline, modern cryptography, which has been mostly developed in the last few decades, is the subject of study which needs to be implemented so as to ensure strong security and privacy mechanisms in today’s real-world scenarios. The technological solutions, short and long term research goals of the cloud security will be described and addressed using various classical cryptographic mechanisms as well as modern ones. This work explores the new directions in cloud computing security, while highlighting the correct selection of these fundamental technologies from cryptographic point of view
To Share or Not to Share in Client-Side Encrypted Clouds
With the advent of cloud computing, a number of cloud providers have arisen
to provide Storage-as-a-Service (SaaS) offerings to both regular consumers and
business organizations. SaaS (different than Software-as-a-Service in this
context) refers to an architectural model in which a cloud provider provides
digital storage on their own infrastructure. Three models exist amongst SaaS
providers for protecting the confidentiality data stored in the cloud: 1) no
encryption (data is stored in plain text), 2) server-side encryption (data is
encrypted once uploaded), and 3) client-side encryption (data is encrypted
prior to upload). This paper seeks to identify weaknesses in the third model,
as it claims to offer 100% user data confidentiality throughout all data
transactions (e.g., upload, download, sharing) through a combination of Network
Traffic Analysis, Source Code Decompilation, and Source Code Disassembly. The
weaknesses we uncovered primarily center around the fact that the cloud
providers we evaluated were each operating in a Certificate Authority capacity
to facilitate data sharing. In this capacity, they assume the role of both
certificate issuer and certificate authorizer as denoted in a Public-Key
Infrastructure (PKI) scheme - which gives them the ability to view user data
contradicting their claims of 100% data confidentiality. We have collated our
analysis and findings in this paper and explore some potential solutions to
address these weaknesses in these sharing methods. The solutions proposed are a
combination of best practices associated with the use of PKI and other
cryptographic primitives generally accepted for protecting the confidentiality
of shared information
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
Data Leak Detection As a Service: Challenges and Solutions
We describe a network-based data-leak detection (DLD)
technique, the main feature of which is that the detection
does not require the data owner to reveal the content of the
sensitive data. Instead, only a small amount of specialized
digests are needed. Our technique – referred to as the fuzzy
fingerprint – can be used to detect accidental data leaks due
to human errors or application flaws. The privacy-preserving
feature of our algorithms minimizes the exposure of sensitive
data and enables the data owner to safely delegate the
detection to others.We describe how cloud providers can offer
their customers data-leak detection as an add-on service
with strong privacy guarantees.
We perform extensive experimental evaluation on the privacy,
efficiency, accuracy and noise tolerance of our techniques.
Our evaluation results under various data-leak scenarios
and setups show that our method can support accurate
detection with very small number of false alarms, even
when the presentation of the data has been transformed. It
also indicates that the detection accuracy does not degrade
when partial digests are used. We further provide a quantifiable
method to measure the privacy guarantee offered by our
fuzzy fingerprint framework
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