BEIJING, July 26,
2024 /PRNewswire/ -- WiMi Hologram Cloud Inc.
(NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram
Augmented Reality ("AR") Technology provider, today announced that
it utilized homomorphic encryption and federated learning building
an advanced data structure architecture. The architecture
integrates federated learning and partial homomorphic encryption,
and this integration protects data privacy while enabling efficient
data analysis and sharing.
Homomorphic encryption is a special encryption technique that
enables computational operations to be performed in an encrypted
state without decrypting the data. By utilizing homomorphic
encryption, it is possible to compute and share data in an
encrypted state while protecting data privacy and integrity, which
is useful for some scenarios involving sensitive data. Federated
learning is a distributed machine learning technique that enables
model improvement by allowing multiple participants to train models
on their respective local datasets without sharing the original
data, and aggregating the learned parameters of these models into a
global model. In data structuring, federated learning can address
the issues of data privacy and data security.
WiMi's data structure architecture based on homomorphic
encryption and federated learning enables data collaboration,
sharing and integration without revealing the original data
content. Participants can train models and update parameters
without direct access to the original data of other participants,
providing an effective and reliable data fusion solution for secure
sharing and analysis of big data. The architecture not only
protects the privacy of data, but also improves the efficiency and
accuracy of data integration. In practical application, firstly,
the requirements of the data architecture need to be analyzed in
detail, including data type, data size, and computational tasks.
Based on the results of the demand analysis, the design objectives
and functions of the data structure are determined. Then,
homomorphic encryption technology is utilized to encrypt the user's
sensitive data to ensure that the data remains encrypted during the
computation process. The encrypted data from the participating
parties are then aggregated and computed using federated learning
techniques. The federated learning process can be implemented using
secure multi-party computation protocols or differential privacy
techniques to ensure data privacy and accuracy of computation
results.
The fusion application of homomorphic encryption and federated
learning is of great significance in the data structure, which can
provide efficient computation and analysis capabilities while
protecting user privacy, bringing more possibilities for technology
utilization in the technology industry. This application is
expected to play an important role in medical and financial fields,
promoting secure data sharing and innovative research, and
promoting the continuous development of the big data field.
For example, in the medical field, patients' medical data often
involves personal privacy, and how to share and analyze medical
data while ensuring data privacy has been a challenge for medical
informatization. WiMi's architecture provides a feasible solution
for secure sharing of medical data by combining federated learning
and homomorphic encryption. Hospitals and research institutes can
work together to train and optimize medical models without
disclosing patients' personal information, improving the quality
and efficiency of medical services. In the financial sector,
financial institutions are faced with a large amount of sensitive
data, such as customer identity information and transaction
records. The leakage of these data may have a serious impact on the
reputation of financial institutions. The data structure
architecture based on homomorphic encryption and federated learning
researched by WiMi can help financial institutions improve the
accuracy and efficiency of their risk control models and
effectively prevent financial risks by encrypting data sharing and
analyzing them under the premise of ensuring data security. In
addition, with the popularization of IoT devices and the
development of social networks, data generation and sharing have
become more and more frequent. How to realize the effective
integration and utilization of data while protecting personal
privacy has become an urgent problem in these fields. The data
structure architecture based on homomorphic encryption and
federated learning provides an effective solution to these
problems.
In the future, WiMi will continue to conduct in-depth research
and development of data structure architecture based on homomorphic
encryption and federated learning and promote the application and
popularization of such architecture in various fields. In the
future, this architecture combining federated learning and
homomorphic encryption will become an important development
direction in the field of big data.
About WIMI Hologram Cloud
WIMI Hologram Cloud, Inc. (NASDAQ:WIMI) is a holographic cloud
comprehensive technical solution provider that focuses on
professional areas including holographic AR automotive HUD
software, 3D holographic pulse LiDAR, head-mounted light field
holographic equipment, holographic semiconductor, holographic cloud
software, holographic car navigation and others. Its services and
holographic AR technologies include holographic AR automotive
application, 3D holographic pulse LiDAR technology, holographic
vision semiconductor technology, holographic software development,
holographic AR advertising technology, holographic AR entertainment
technology, holographic ARSDK payment, interactive holographic
communication and other holographic AR technologies.
Safe Harbor Statements
This press release contains "forward-looking statements" within
the Private Securities Litigation Reform Act of 1995. These
forward-looking statements can be identified by terminology such as
"will," "expects," "anticipates," "future," "intends," "plans,"
"believes," "estimates," and similar statements. Statements that
are not historical facts, including statements about the Company's
beliefs and expectations, are forward-looking statements. Among
other things, the business outlook and quotations from management
in this press release and the Company's strategic and operational
plans contain forward−looking statements. The Company may also make
written or oral forward−looking statements in its periodic reports
to the US Securities and Exchange Commission ("SEC") on Forms 20−F
and 6−K, in its annual report to shareholders, in press releases,
and other written materials, and in oral statements made by its
officers, directors or employees to third parties. Forward-looking
statements involve inherent risks and uncertainties. Several
factors could cause actual results to differ materially from those
contained in any forward−looking statement, including but not
limited to the following: the Company's goals and strategies; the
Company's future business development, financial condition, and
results of operations; the expected growth of the AR holographic
industry; and the Company's expectations regarding demand for and
market acceptance of its products and services.
Further information regarding these and other risks is included
in the Company's annual report on Form 20-F and the current report
on Form 6-K and other documents filed with the SEC. All information
provided in this press release is as of the date of this press
release. The Company does not undertake any obligation to update
any forward-looking statement except as required under applicable
laws.
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SOURCE WiMi Hologram Cloud Inc.