Utility-preserving differentially private data releases via individual ranking microaggregation

Background: As artificial intelligence and big data analysis develop rapidly, data privacy, especially patient medical data privacy, is getting more and more attention. Objective: To strengthen the protection of private data while ensuring the model training process, this article introduces a multi-Blockchain-based decentralized collaborative machine learning training method for medical image analysis. In this way, researchers from different medical institutions are able to collaborate to train models without exchanging sensitive patient data. Method: Partial parameter update method is applied to prevent indirect privacy leakage during model propagation. With the peer-to-peer communication in the multi-Blockchain system, a machine learning task can leverage auxiliary information from another similar task in another Blockchain. In addition, after the collaborative training process, personalized models of different medical institutions will be trained. Results: The experimental results show that our method achieves similar performance with the centralized model-training method by collecting data sets of all participants and prevents private data leakage at the same time. Transferring auxiliary information from similar task on another Blockchain has also been proven to effectively accelerate model convergence and improve model accuracy, especially in the scenario of absence of data. Personalization training process further improves model performance. Conclusion: Our approach can effectively help researchers from different organizations to achieve collaborative training without disclosing their private data.

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Generating Private Data Surrogates for Vision Related Tasks

2020 25th International Conference on Pattern Recognition (ICPR) ◽

10.1109/icpr48806.2021.9413067 ◽

2021 ◽

Author(s):

Ryan Webster ◽

Julien Rabin ◽

Loic Simon ◽

Frederic Jurie

Keyword(s):

Private Data

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Private Data Manipulation in Optimal Sponsored Search Auction

Proceedings of The Web Conference 2020 ◽

10.1145/3366423.3380023 ◽

2020 ◽

Cited By ~ 1

Author(s):

Xiaotie Deng ◽

Tao Lin ◽

Tao Xiao

Keyword(s):

Federated Learning in a Medical Context: A Systematic Literature Review

ACM Transactions on Internet Technology ◽

10.1145/3412357 ◽

2021 ◽

Vol 21 (2) ◽

pp. 1-31

Author(s):

Bjarne Pfitzner ◽

Nico Steckhan ◽

Bert Arnrich

Keyword(s):

Machine Learning ◽

Literature Review ◽

Systematic Literature Review ◽

Data Privacy ◽

Research Area ◽

Learning Models ◽

Related Data ◽

Private Data ◽

Large Databases ◽

Machine Learning Models

Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients’ anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets.

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Efficient Private Conjunctive Query Protocol Over Encrypted Data

Cryptography ◽

10.3390/cryptography5010002 ◽

2021 ◽

Vol 5 (1) ◽

pp. 2

Author(s):

Tushar Kanti Saha ◽

Takeshi Koshiba

Keyword(s):

Homomorphic Encryption ◽

Data Access ◽

Conjunctive Query ◽

Conjunctive Queries ◽

Private Data ◽

Binary Format ◽

Main Technique ◽

Batch Technique ◽

Outsourced Database ◽

Packing Method

Conjunctive queries play a key role in retrieving data from a database. In a database, a query containing many conditions in its predicate, connected by an “and/&/∧” operator, is called a conjunctive query. Retrieving the outcome of a conjunctive query from thousands of records is a heavy computational task. Private data access to an outsourced database is required to keep the database secure from adversaries; thus, private conjunctive queries (PCQs) are indispensable. Cheon, Kim, and Kim (CKK) proposed a PCQ protocol using search-and-compute circuits in which they used somewhat homomorphic encryption (SwHE) for their protocol security. As their protocol is far from being able to be used practically, we propose a practical batch private conjunctive query (BPCQ) protocol by applying a batch technique for processing conjunctive queries over an outsourced database, in which both database and queries are encoded in binary format. As a main technique in our protocol, we develop a new data-packing method to pack many data into a single polynomial with the batch technique. We further enhance the performances of the binary-encoded BPCQ protocol by replacing the binary encoding with N-ary encoding. Finally, we compare the performance to assess the results obtained by the binary-encoded BPCQ protocol and the N-ary-encoded BPCQ protocol.

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A Design Framework for Strongly χ²-Private Data Disclosure

IEEE Transactions on Information Forensics and Security ◽

10.1109/tifs.2021.3053462 ◽

2021 ◽

Vol 16 ◽

pp. 2312-2325

Author(s):

Amirreza Zamani ◽

Tobias J. Oechtering ◽

Mikael Skoglund

Keyword(s):

Design Framework ◽

Private Data

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Sustainable Security for the Internet of Things Using Artificial Intelligence Architectures

ACM Transactions on Internet Technology ◽

10.1145/3448614 ◽

2021 ◽

Vol 21 (3) ◽

pp. 1-22

Author(s):

Celestine Iwendi ◽

Saif Ur Rehman ◽

Abdul Rehman Javed ◽

Suleman Khan ◽

Gautam Srivastava

Keyword(s):

Internet Of Things ◽

Detection System ◽

Experimental Testing ◽

Cyber Attacks ◽

Internet Technology ◽

The Internet ◽

Business Operations ◽

Private Data ◽

The Internet Of Things ◽

Better Than

In this digital age, human dependency on technology in various fields has been increasing tremendously. Torrential amounts of different electronic products are being manufactured daily for everyday use. With this advancement in the world of Internet technology, cybersecurity of software and hardware systems are now prerequisites for major business’ operations. Every technology on the market has multiple vulnerabilities that are exploited by hackers and cyber-criminals daily to manipulate data sometimes for malicious purposes. In any system, the Intrusion Detection System (IDS) is a fundamental component for ensuring the security of devices from digital attacks. Recognition of new developing digital threats is getting harder for existing IDS. Furthermore, advanced frameworks are required for IDS to function both efficiently and effectively. The commonly observed cyber-attacks in the business domain include minor attacks used for stealing private data. This article presents a deep learning methodology for detecting cyber-attacks on the Internet of Things using a Long Short Term Networks classifier. Our extensive experimental testing show an Accuracy of 99.09%, F1-score of 99.46%, and Recall of 99.51%, respectively. A detailed metric representing our results in tabular form was used to compare how our model was better than other state-of-the-art models in detecting cyber-attacks with proficiency.

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