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An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors [article]

Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin
2019 arXiv   pre-print
Differential privacy has emerged as the main definition for private data analysis and machine learning.  ...  In this work, we propose a framework based on trusted processors and a new definition of differential privacy called Oblivious Differential Privacy, which combines the best of both local and global models  ...  To this end, our framework is similar to other systems for private data analysis based on trusted processors, including machine learning algorithms [41] and data analytical platforms such as PROCHLO  ... 
arXiv:1807.00736v2 fatcat:dyoheafz2vhrvootoagmwa5xyi

A Review of Physical Attacks and Trusted Platforms in Wireless Sensor Networks

Yusnani Mohd Yussoff, Habibah Hashim, Roszainiza Rosli, Mohd Dani Baba
2012 Procedia Engineering  
Wireless Sensor Networks (WSNs) have shown great promise as the emerging technology for data gathering from unattended or hostile environment.  ...  Acknowledging the severity of such attacks, this paper first presents the review on physical attacks followed by the introduction of trusted platform with protected memory that not only protect sensor  ...  Acknowledgements The authors would like to thank Universiti Teknologi MARA (UiTM) and Research Management Institute (RMI) for providing the research grant for this research work.  ... 
doi:10.1016/j.proeng.2012.07.215 fatcat:chlsheeqbfc6lle2yeq4m6gppi

Identity-based Trusted Authentication in Wireless Sensor Network [article]

Yusnani Mohd Yussoff, Habibah Hashim, Mohd Dani Baba
2012 arXiv   pre-print
In this study an alternative mechanism is proposed to accomplish trusted communication between sensors based on the principles defined by Trusted Computing Group (TCG).  ...  Finally the proposed trusted mechanism is evaluated for the potential application on resource constraint devices by quantifying their power consumption on selected major processes.  ...  Finally it presents an analysis on the energy consumption for the trusted platform and the authentication protocol.  ... 
arXiv:1207.6185v1 fatcat:oydchw7qyna4pmsdgvji7va7yu

Efficient Deep Learning on Multi-Source Private Data [article]

Nick Hynes, Raymond Cheng, Dawn Song
2018 arXiv   pre-print
In this work, we introduce Myelin, a deep learning framework which combines these privacy-preservation primitives, and use it to establish a baseline level of performance for fully private machine learning  ...  Using such datasets, however, often requires trusting a centralized data aggregator.  ...  Differential Privacy Applying differential privacy to an algorithm provides a strong bound on how much information it can leak about any item of input data.  ... 
arXiv:1807.06689v1 fatcat:7rkxur5qazasxisnecekjkjely

Towards an Architecture to Guarantee Both Data Privacy and Utility in the First Phases of Digital Clinical Trials

Fabio Angeletti, Ioannis Chatzigiannakis, Andrea Vitaletti
2018 Sensors  
Current approaches in digital trials entail that private user data are provisioned to the trial investigator that is considered a trusted party.  ...  The proposed architecture will let the individuals keep their data private during these phases while providing a useful sketch of their data to the investigator.  ...  More formally, given a randomized algorithm A and two datasets D1 and D2 that differ in exactly one record (i.e., the data of one person), A is -differential private if for any S ⊆ Range(A) Pr[A(D1)  ... 
doi:10.3390/s18124175 pmid:30487435 fatcat:6s3bgzgthjctdekupawvxp2x4m

Abcd Analysis Of Fingerprint Hash Code, Password And Otp Based Multifactor Authentication Model

P. S. Aithal, Krishna Prasad
2018 Zenodo  
The analysis has brought out many critical constituent elements, which is one of the proofs for the success of the new methodology  ...  In this paper, as per ABCD analysis various determinant issues related to Multifactor Authentication Model for Verification/Authentication purpose are: (1) Security issues, (2) User-friendly issues, (3  ...  biometric performance evaluation Performance evaluation matrices of biometrics data Model based on Fingerprint Hash Code, Password, and OTP using ABCD analysis framework.  ... 
doi:10.5281/zenodo.1202336 fatcat:tukm7jhuf5hfbnk4zwgmmk3k5i

Automating Open Science for Big Data

Mercè Crosas, Gary King, James Honaker, Latanya Sweeney, Dhavan V. Shah, Joseph N. Cappella, W. Russell Neuman
2015 The Annals of the American Academy of Political and Social Science  
Curator models and differential privacy A curator model for privacy preservation supposes a trusted intermediary who has full access to private data, and a system for submitting and replying to queries  ...  In this framework, any reported results generated by Zelig would be processed through an algorithm that ensures differential privacy, to the degree of privacy required, and as elicited from the Datatags  ... 
doi:10.1177/0002716215570847 fatcat:kn2qrwd3e5hwpp225kenjyqpya

Precision Health Data: Requirements, Challenges and Existing Techniques for Data Security and Privacy [article]

Chandra Thapa, Seyit Camtepe
2020 arXiv   pre-print
Thus, the security, privacy of and trust on the information are of utmost importance. Moreover, government legislation and ethics committees demand the security and privacy of healthcare data.  ...  Herein, in the light of precision health data security, privacy, ethical and regulatory requirements, finding the best methods and techniques for the utilization of the health data, and thus precision  ...  Thus there can be a significant privacy loss when multiple differentially private computations are performed on an individual's data for a long time.  ... 
arXiv:2008.10733v1 fatcat:oj2neoftf5hcbpatnfn7ntyhzy

Flatee: Federated Learning Across Trusted Execution Environments [article]

Arup Mondal and Yash More and Ruthu Hulikal Rooparaghunath and Debayan Gupta
2021 arXiv   pre-print
In this paper, we propose the use of Trusted Execution Environments (TEE), which provide a platform for isolated execution of code and handling of data, for this purpose.  ...  We describe Flatee, an efficient privacy-preserving federated learning framework across TEEs, which considerably reduces training and communication time.  ...  An FL task is a specific computation for an FL population, such as training to be performed with given hyperparameters, or evaluation of trained models on local device data.  ... 
arXiv:2111.06867v1 fatcat:rl2rysly7fcbtnw32acea4liu4

Integrity Enhancement in Wireless Sensor Networks [chapter]

Yusnani Mohd, Habibah Hashim, Husna Zainol
2010 Smart Wireless Sensor Networks  
However, sensor networks make large-scale attacks become trivial when private information on the entire system can instantly reach the hand of attackers.  ...  However, latest technology in embedded security combined (low power, on-SOC memory, small size) with trusted computing specifications (ensuring trusted communication and user) is believed to enhance security  ...  All important keys and data will be saved in the On-SoC memory thus provide better shielding to private information on the platform.  ... 
doi:10.5772/13712 fatcat:r65jbllfzzc6bjgd5kxinkgmlu

A Distributed Trust Framework for Privacy-Preserving Machine Learning [article]

Will Abramson, Adam James Hall, Pavlos Papadopoulos, Nikolaos Pitropakis, William J Buchanan
2020 arXiv   pre-print
However, this engenders a lack of trust between data owners and data scientists. Data owners are justifiably reluctant to relinquish control of private information to third parties.  ...  However, architectures distributed amongst multiple agents introduce an entirely new set of security and trust complications. These include data poisoning and model theft.  ...  An organisation providing ML capabilities needs data to train, test and validate their algorithm. However, data owners tend to be wary of sharing data with third-party processors.  ... 
arXiv:2006.02456v1 fatcat:hhz3aqx7prhplmurt4i23u7jq4

A Comprehensive Analysis of Privacy-preserving Techniques in Deep learning based Disease Prediction Systems

J Andrew, Shaun Shibu Mathew, Batra Mohit
2019 Journal of Physics, Conference Series  
With the rise in demand for deep learning models due to its ability to learn features from data, and predict, it is widely used in disease prediction systems.  ...  In this paper we carry out a comprehensive analysis of privacy-preserving techniques for disease prediction systems that use deep learning along with a comparison of the different privacy-preserving techniques  ...  This brings about an improvement in the accuracy of differentially private K-means clustering algorithm.  ... 
doi:10.1088/1742-6596/1362/1/012070 fatcat:qi6ytovxzbflfficpa4ifkd7gu

An Advanced Private Social Activity Invitation Framework with Friendship Protection

Weitian Tong, Lei Chen, Scott Buglass, Weinan Gao, Jeffrey Li
2017 Wireless Communications and Mobile Computing  
for graph publication, which addresses an open concern proposed recently; (3) presenting an efficient invitee-selection algorithm, which outperforms the existing ones.  ...  To the best of our knowledge, it is the first work to publish a directed graph in a differentially private manner with an untrustworthy server.  ...  ( i ) i If all M are defined on the same data set, then M [ ] is (∑ =1 )-differentially private. (ii) If all M are defined on different data sets, then M [ ] is (max{ })-differentially private.  ... 
doi:10.1155/2017/1393026 fatcat:4uxruw4curfxfi2p6slbqianv4

Prochlo

Andrea Bittau, Bernhard Seefeld, Úlfar Erlingsson, Petros Maniatis, Ilya Mironov, Ananth Raghunathan, David Lie, Mitch Rudominer, Ushasree Kode, Julien Tinnes
2017 Proceedings of the 26th Symposium on Operating Systems Principles - SOSP '17  
., for application telemetry, error reporting, or demographic profiling.  ...  The ESA design, and its Prochlo implementation, are informed by our practical experiences with an existing, large deployment of privacy-preserving software monitoring. (cont.; see the paper)  ...  We thank the anonymous reviewers for their detailed feedback, and Martín Abadi, Johannes Gehrke, Lea Kissner, Noé Lutz, and Nicolas Papernot for their valuable advice on earlier drafts.  ... 
doi:10.1145/3132747.3132769 dblp:conf/sosp/BittauEMMRLRKTS17 fatcat:s5icmqnn6jgznjqhh4fo4fzahm

Syft 0.5: A Platform for Universally Deployable Structured Transparency [article]

Adam James Hall, Madhava Jay, Tudor Cebere, Bogdan Cebere, Koen Lennart van der Veen, George Muraru, Tongye Xu, Patrick Cason, William Abramson, Ayoub Benaissa, Chinmay Shah, Alan Aboudib (+11 others)
2021 arXiv   pre-print
neural network for inference.  ...  We present Syft 0.5, a general-purpose framework that combines a core group of privacy-enhancing technologies that facilitate a universal set of structured transparency systems.  ...  PyDP -The application of statistical is a Python wrapper for Google's Differential Privacy project. The library provides a set of -differentially private algorithms.  ... 
arXiv:2104.12385v2 fatcat:duc4q2pwpnbabpremsfmojhp3i
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