377 research outputs found
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
Secured Uploading and Retrieval of Data Using Visual Cryptography Scheme
Cloud storage provides a convenient, massive, and scalable storage at low cost, but data security is a major issue that prevents users from storing ?les on the cloud. This paper focuses on security for the documents that are uploaded and stored on the cloud. However, it poses risks to end users unless the data is encrypted for security. This study addresses these issues by proposing Visual Cryptography Scheme (VCS) for securing the files. In order to prevent issues like breaches and malware attacks on cloud, this innovative scheme helps in high level security to safeguard the files that are stored on the clou
Preserving Both Privacy and Utility in Network Trace Anonymization
As network security monitoring grows more sophisticated, there is an
increasing need for outsourcing such tasks to third-party analysts. However,
organizations are usually reluctant to share their network traces due to
privacy concerns over sensitive information, e.g., network and system
configuration, which may potentially be exploited for attacks. In cases where
data owners are convinced to share their network traces, the data are typically
subjected to certain anonymization techniques, e.g., CryptoPAn, which replaces
real IP addresses with prefix-preserving pseudonyms. However, most such
techniques either are vulnerable to adversaries with prior knowledge about some
network flows in the traces, or require heavy data sanitization or
perturbation, both of which may result in a significant loss of data utility.
In this paper, we aim to preserve both privacy and utility through shifting the
trade-off from between privacy and utility to between privacy and computational
cost. The key idea is for the analysts to generate and analyze multiple
anonymized views of the original network traces; those views are designed to be
sufficiently indistinguishable even to adversaries armed with prior knowledge,
which preserves the privacy, whereas one of the views will yield true analysis
results privately retrieved by the data owner, which preserves the utility. We
present the general approach and instantiate it based on CryptoPAn. We formally
analyze the privacy of our solution and experimentally evaluate it using real
network traces provided by a major ISP. The results show that our approach can
significantly reduce the level of information leakage (e.g., less than 1\% of
the information leaked by CryptoPAn) with comparable utility
A secure privacy preserving deduplication scheme for cloud computing
© 2019 Elsevier B.V. Data deduplication is a key technique to improve storage efficiency in cloud computing. By pointing redundant files to a single copy, cloud service providers greatly reduce their storage space as well as data transfer costs. Despite of the fact that the traditional deduplication approach has been adopted widely, it comes with a high risk of losing data confidentiality because of the data storage models in cloud computing. To deal with this issue in cloud storage, we first propose a TEE (trusted execution environment) based secure deduplication scheme. In our scheme, each cloud user is assigned a privilege set; the deduplication can be performed if and only if the cloud users have the correct privilege. Moreover, our scheme augments the convergent encryption with users’ privileges and relies on TEE to provide secure key management, which improves the ability of such cryptosystem to resist chosen plaintext attacks and chosen ciphertext attacks. A security analysis indicates that our scheme is secure enough to support data deduplication and to protect the confidentiality of sensitive data. Furthermore, we implement a prototype of our scheme and evaluate the performance of our prototype, experiments show that the overhead of our scheme is practical in realistic environments
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
Routes for breaching and protecting genetic privacy
We are entering the era of ubiquitous genetic information for research,
clinical care, and personal curiosity. Sharing these datasets is vital for
rapid progress in understanding the genetic basis of human diseases. However,
one growing concern is the ability to protect the genetic privacy of the data
originators. Here, we technically map threats to genetic privacy and discuss
potential mitigation strategies for privacy-preserving dissemination of genetic
data.Comment: Draft for comment
Information Security: A Coordinated Strategy to Guarantee Data Security in Cloud Computing
This paper discusses different techniques and specialized procedures which can be used to effectively protect data from the owner to the cloud and then to the user. The next step involves categorizing the data using three encryption parameters provided by the user, which are Integrity, Availability, and Confidentiality (IAC). The data is secured through various methods such as SSL and MAC protocols to ensure data integrity checks, searchable encryption, and splitting the data into three parts for cloud storage. Dividing the data into three portions not only enhances security but also facilitates easier access. Access to the encrypted data requires the user to provide the login information and password of the owner. This paper also studies critical security issues like unauthorized servers, brute force attacks, threats from cloud service providers, and loss of user identity and password
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