2,664 research outputs found
Exploring Privacy Preservation in Outsourced K-Nearest Neighbors with Multiple Data Owners
The k-nearest neighbors (k-NN) algorithm is a popular and effective
classification algorithm. Due to its large storage and computational
requirements, it is suitable for cloud outsourcing. However, k-NN is often run
on sensitive data such as medical records, user images, or personal
information. It is important to protect the privacy of data in an outsourced
k-NN system.
Prior works have all assumed the data owners (who submit data to the
outsourced k-NN system) are a single trusted party. However, we observe that in
many practical scenarios, there may be multiple mutually distrusting data
owners. In this work, we present the first framing and exploration of privacy
preservation in an outsourced k-NN system with multiple data owners. We
consider the various threat models introduced by this modification. We discover
that under a particularly practical threat model that covers numerous
scenarios, there exists a set of adaptive attacks that breach the data privacy
of any exact k-NN system. The vulnerability is a result of the mathematical
properties of k-NN and its output. Thus, we propose a privacy-preserving
alternative system supporting kernel density estimation using a Gaussian
kernel, a classification algorithm from the same family as k-NN. In many
applications, this similar algorithm serves as a good substitute for k-NN. We
additionally investigate solutions for other threat models, often through
extensions on prior single data owner systems
Mining Privacy-Preserving Association Rules based on Parallel Processing in Cloud Computing
With the onset of the Information Era and the rapid growth of information
technology, ample space for processing and extracting data has opened up.
However, privacy concerns may stifle expansion throughout this area. The
challenge of reliable mining techniques when transactions disperse across
sources is addressed in this study. This work looks at the prospect of creating
a new set of three algorithms that can obtain maximum privacy, data utility,
and time savings while doing so. This paper proposes a unique double encryption
and Transaction Splitter approach to alter the database to optimize the data
utility and confidentiality tradeoff in the preparation phase. This paper
presents a customized apriori approach for the mining process, which does not
examine the entire database to estimate the support for each attribute.
Existing distributed data solutions have a high encryption complexity and an
insufficient specification of many participants' properties. Proposed solutions
provide increased privacy protection against a variety of attack models.
Furthermore, in terms of communication cycles and processing complexity, it is
much simpler and quicker. Proposed work tests on top of a realworld transaction
database demonstrate that the aim of the proposed method is realistic
A Privacy-Preserving Framework for Collaborative Association Rule Mining in Cloud
Collaborative Data Mining facilitates multiple organizations to integrate their datasets and extract useful knowledge from their joint datasets for mutual benefits. The knowledge extracted in this manner is found to be superior to the knowledge extracted locally from a single organization’s dataset. With the rapid development of outsourcing, there is a growing interest for organizations to outsource their data mining tasks to a cloud environment to effectively address their economic and performance demands. However, due to privacy concerns and stringent compliance regulations, organizations do not want to share their private datasets neither with the cloud nor with other participating organizations. In this paper, we address the problem of outsourcing association rule mining task to a federated cloud environment in a privacy-preserving manner. Specifically, we propose a privacy-preserving framework that allows a set of users, each with a private dataset, to outsource their encrypted databases and the cloud returns the association rules extracted from the aggregated encrypted databases to the participating users. Our proposed solution ensures the confidentiality of the outsourced data and also minimizes the users’ participation during the association rule mining process. Additionally, we show that the proposed solution is secure under the standard semi-honest model and demonstrate its practicality
Hybrid Cloud-Based Privacy Preserving Clustering as Service for Enterprise Big Data
Clustering as service is being offered by many cloud service providers. It helps enterprises to learn hidden patterns and learn knowledge from large, big data generated by enterprises. Though it brings lot of value to enterprises, it also exposes the data to various security and privacy threats. Privacy preserving clustering is being proposed a solution to address this problem. But the privacy preserving clustering as outsourced service model involves too much overhead on querying user, lacks adaptivity to incremental data and involves frequent interaction between service provider and the querying user. There is also a lack of personalization to clustering by the querying user. This work “Locality Sensitive Hashing for Transformed Dataset (LSHTD)” proposes a hybrid cloud-based clustering as service model for streaming data that address the problems in the existing model such as privacy preserving k-means clustering outsourcing under multiple keys (PPCOM) and secure nearest neighbor clustering (SNNC) models, The solution combines hybrid cloud, LSHTD clustering algorithm as outsourced service model. Through experiments, the proposed solution is able is found to reduce the computation cost by 23% and communication cost by 6% and able to provide better clustering accuracy with ARI greater than 4.59% compared to existing works
Outsourced Analysis of Encrypted Graphs in the Cloud with Privacy Protection
Huge diagrams have unique properties for organizations and research, such as
client linkages in informal organizations and customer evaluation lattices in
social channels. They necessitate a lot of financial assets to maintain because
they are large and frequently continue to expand. Owners of large diagrams may
need to use cloud resources due to the extensive arrangement of open cloud
resources to increase capacity and computation flexibility. However, the
cloud's accountability and protection of schematics have become a significant
issue. In this study, we consider calculations for security savings for
essential graph examination practices: schematic extraterrestrial examination
for outsourcing graphs in the cloud server. We create the security-protecting
variants of the two proposed Eigen decay computations. They are using two
cryptographic algorithms: additional substance homomorphic encryption (ASHE)
strategies and some degree homomorphic encryption (SDHE) methods. Inadequate
networks also feature a distinctively confidential info adaptation convention
to allow the trade-off between secrecy and data sparseness. Both dense and
sparse structures are investigated. According to test results, calculations
with sparse encoding can drastically reduce information. SDHE-based strategies
have reduced computing time, while ASHE-based methods have reduced stockpiling
expenses
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
Fast Search Processing Over Encrypted Relational Data Using K-Nearest Neighbour Algorithm
Data mining has been used in real time application in a number of areas such as for example financial, telecommunication, biological, and among government agencies and several application handle very sensitive data. So these data remains secure and private.Data encryption is a very strong option to secure the data in databases from unauthorized access and intruder.The previous privacy preserving classification techniques are not feasible for encrypted data of database.In this paper, our proposed method provides privacy-preserving classifier for encrypted data of relational databasesand achieves the better performance for extracting information from encrypted data of relational databases
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