765 research outputs found
Privacy Preserving ID3 over Horizontally, Vertically and Grid Partitioned Data
We consider privacy preserving decision tree induction via ID3 in the case
where the training data is horizontally or vertically distributed. Furthermore,
we consider the same problem in the case where the data is both horizontally
and vertically distributed, a situation we refer to as grid partitioned data.
We give an algorithm for privacy preserving ID3 over horizontally partitioned
data involving more than two parties. For grid partitioned data, we discuss two
different evaluation methods for preserving privacy ID3, namely, first merging
horizontally and developing vertically or first merging vertically and next
developing horizontally. Next to introducing privacy preserving data mining
over grid-partitioned data, the main contribution of this paper is that we
show, by means of a complexity analysis that the former evaluation method is
the more efficient.Comment: 25 page
Privacy-Preserving Decision Tree Classification over Horizontally Partitioned Data
Protection of privacy is one of important problems in data mining. The unwillingness to share their data frequently results in failure of collaborative data mining. This paper studies how to build a decision tree classifier under the following scenario: a database is horizontally partitioned into multiple pieces, with each piece owned by a particular party. All the parties want to build a decision tree classifier based on such a database, but due to the privacy constraints, neither of them wants to disclose their private pieces. We build a privacy-preserving system, including a set of secure protocols, that allows the parties to construct such a classifier. We guarantee that the private data are securely protected
Efficient Privacy Preserving Distributed Clustering Based on Secret Sharing
In this paper, we propose a privacy preserving distributed
clustering protocol for horizontally partitioned data based on a very efficient
homomorphic additive secret sharing scheme. The model we use
for the protocol is novel in the sense that it utilizes two non-colluding
third parties. We provide a brief security analysis of our protocol from
information theoretic point of view, which is a stronger security model.
We show communication and computation complexity analysis of our
protocol along with another protocol previously proposed for the same
problem. We also include experimental results for computation and communication
overhead of these two protocols. Our protocol not only outperforms
the others in execution time and communication overhead on
data holders, but also uses a more efficient model for many data mining
applications
An Enhanced CART Algorithm for Preserving Privacy of Distributed Data and Provide Access Control over Tree Data
Now in these days the utilization of distributed applications are increases rapidly because these applications are serve more than one client at a time. In the use of distributed database data distribution and management is a key area of attraction. Because of privacy of private data organizations are unwilling to participate for data mining due to the data leakage. So it is required to collect data from different parties in a secured way. This paper represents how CART algorithm can be used for multi parties in vertically partitioned environment. In order to solve the privacy and security issues the proposed model incorporates the server side random key generation and key distribution. Finally the performance of proposed classification technique is evaluated in terms of memory consumption, training time, search time, accuracy and there error rate
Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations
In the growing world of artificial intelligence, federated learning is a
distributed learning framework enhanced to preserve the privacy of individuals'
data. Federated learning lays the groundwork for collaborative research in
areas where the data is sensitive. Federated learning has several implications
for real-world problems. In times of crisis, when real-time decision-making is
critical, federated learning allows multiple entities to work collectively
without sharing sensitive data. This distributed approach enables us to
leverage information from multiple sources and gain more diverse insights. This
paper is a systematic review of the literature on privacy-preserving machine
learning in the last few years based on the Preferred Reporting Items for
Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Specifically, we have
presented an extensive review of supervised/unsupervised machine learning
algorithms, ensemble methods, meta-heuristic approaches, blockchain technology,
and reinforcement learning used in the framework of federated learning, in
addition to an overview of federated learning applications. This paper reviews
the literature on the components of federated learning and its applications in
the last few years. The main purpose of this work is to provide researchers and
practitioners with a comprehensive overview of federated learning from the
machine learning point of view. A discussion of some open problems and future
research directions in federated learning is also provided
Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection
The effective detection of evidence of financial anomalies requires
collaboration among multiple entities who own a diverse set of data, such as a
payment network system (PNS) and its partner banks. Trust among these financial
institutions is limited by regulation and competition. Federated learning (FL)
enables entities to collaboratively train a model when data is either
vertically or horizontally partitioned across the entities. However, in
real-world financial anomaly detection scenarios, the data is partitioned both
vertically and horizontally and hence it is not possible to use existing FL
approaches in a plug-and-play manner.
Our novel solution, PV4FAD, combines fully homomorphic encryption (HE),
secure multi-party computation (SMPC), differential privacy (DP), and
randomization techniques to balance privacy and accuracy during training and to
prevent inference threats at model deployment time. Our solution provides input
privacy through HE and SMPC, and output privacy against inference time attacks
through DP. Specifically, we show that, in the honest-but-curious threat model,
banks do not learn any sensitive features about PNS transactions, and the PNS
does not learn any information about the banks' dataset but only learns
prediction labels. We also develop and analyze a DP mechanism to protect output
privacy during inference. Our solution generates high-utility models by
significantly reducing the per-bank noise level while satisfying distributed
DP. To ensure high accuracy, our approach produces an ensemble model, in
particular, a random forest. This enables us to take advantage of the
well-known properties of ensembles to reduce variance and increase accuracy.
Our solution won second prize in the first phase of the U.S. Privacy Enhancing
Technologies (PETs) Prize Challenge.Comment: Prize Winner in the U.S. Privacy Enhancing Technologies (PETs) Prize
Challeng
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