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Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text [article]

Oluwaseyi Feyisetan and Tom Diethe and Thomas Drake
2019 arXiv   pre-print
In this work, we explore word representations in Hyperbolic space as a means of preserving privacy in text.  ...  We conduct a series of experiments to demonstrate the tradeoff between privacy and utility.  ...  The privacy mechanism in Euclidean space Our d χ -privacy algorithm is similar to the model introduced by [29] for privacy preserving text analysis, and [30] for author obfuscation.  ... 
arXiv:1910.08917v1 fatcat:e4jedtnlozcc3kztgzqvklrxii

TEM: High Utility Metric Differential Privacy on Text [article]

Ricardo Silva Carvalho, Theodore Vasiloudis, Oluwaseyi Feyisetan
2021 arXiv   pre-print
In our experiments, we demonstrate that our method significantly outperforms the state-of-the-art in terms of utility for the same level of privacy, while providing more flexibility in the metric selection  ...  The most successful previous methods use a generalization of DP for metric spaces, and apply the privatization by adding noise to inputs in the metric space of word embeddings.  ...  Oluwaseyi Feyisetan, Tom Diethe, and Thomas Drake. Leveraging hierarchical representations for preserving privacy and utility in text.  ... 
arXiv:2107.07928v1 fatcat:f64dr4y7bzek7bmixjtw4es5uy

BRR: Preserving Privacy of Text Data Efficiently on Device [article]

Ricardo Silva Carvalho, Theodore Vasiloudis, Oluwaseyi Feyisetan
2021 arXiv   pre-print
In this work we propose an efficient mechanism to provide metric differential privacy for text data on-device.  ...  We compare our algorithm to the state-of-the-art for text privatization, showing similar or better utility for the same privacy guarantees, while reducing the storage costs by orders of magnitude, enabling  ...  This mechanism works in a hierarchical embedding space, where the embedding vector of an input word is perturbed with noise from a hyperbolic distribution.  ... 
arXiv:2107.07923v1 fatcat:kgnpj6m2zvflrlknwtmvdlbaxi

CAPE: Context-Aware Private Embeddings for Private Language Learning [article]

Richard Plant, Dimitra Gkatzia, Valerio Giuffrida
2021 arXiv   pre-print
To ameliorate these issues, we propose Context-Aware Private Embeddings (CAPE), a novel approach which preserves privacy during training of embeddings.  ...  To maintain the privacy of text representations, CAPE applies calibrated noise through differential privacy, preserving the encoded semantic links while obscuring sensitive information.  ...  ,Table 1: Results for the target task and the simulated attacker task. CAPE outperforms all other approaches in terms of privacy-preservation for all variables.  ... 
arXiv:2108.12318v1 fatcat:qs254y4bdvhejhpk4uuaz33uva

Research Challenges in Designing Differentially Private Text Generation Mechanisms

Oluwaseyi Feyisetan, Abhinav Aggarwal, Zekun Xu, Nathanael Teissier
2021 Proceedings of the ... International Florida Artificial Intelligence Research Society Conference  
Such mechanisms add privacy preserving noise to vectorial representations of text in high dimension and return a text based projection of the noisy vectors.  ...  However, these mechanisms are sub-optimal in their trade-off between privacy and utility. In this proposal paper, we describe some challenges in balancing this trade-off.  ...  Similarly, (Feyisetan, Diethe, and Drake 2019) extended the model to demonstrate preserving privacy using noise sampled from Hyperbolic space.  ... 
doi:10.32473/flairs.v34i1.128461 fatcat:mcse4lzmjjfcveqgptzlgrttiy

Research Challenges in Designing Differentially Private Text Generation Mechanisms [article]

Oluwaseyi Feyisetan, Abhinav Aggarwal, Zekun Xu, Nathanael Teissier
2020 arXiv   pre-print
In this proposal paper, we describe some challenges in balancing the tradeoff between privacy and utility for these differentially private text mechanisms.  ...  Such mechanisms add privacy preserving noise to vectorial representations of text in high dimension and return a text based projection of the noisy vectors.  ...  In this paper we propose strategies for increasing the utility of these 'DP for text' mechanisms by reducing the noise required while maintaining the desired privacy guarantees.  ... 
arXiv:2012.05403v1 fatcat:5hj677zgzzhmllyepa3bmxzcnu

The Limits of Word Level Differential Privacy [article]

Justus Mattern, Benjamin Weggenmann, Florian Kerschbaum
2022 arXiv   pre-print
Finally, we propose a new method for text anonymization based on transformer based language models fine-tuned for paraphrasing that circumvents most of the identified weaknesses and also offers a formal  ...  A significant subset of these approaches incorporate differentially private mechanisms to perturb word embeddings, thus replacing individual words in a sentence.  ...  Acknowledgements We thank Zhijing Jin for the helpful discussions about the presentation of our results and the design of our paper.  ... 
arXiv:2205.02130v1 fatcat:vim2hzhtujbpxbqcytqaz6n57e

A^4NT: Author Attribute Anonymity by Adversarial Training of Neural Machine Translation [article]

Rakshith Shetty and Bernt Schiele and Mario Fritz
2018 arXiv   pre-print
Importantly, we propose and evaluate techniques to impose constraints on our A^4NT to preserve the semantics of the input text.  ...  Text-based analysis methods allow to reveal privacy relevant author attributes such as gender, age and identify of the text's author.  ...  Acknowledgment This research was supported in part by the German Research Foundation (DFG CRC 1223). We would also like to thank Yang Zhang, Ben Stock and Sven Bugiel for helpful feedback.  ... 
arXiv:1711.01921v3 fatcat:s3wnqeufrbcgtcp4pntljugff4

Privacy Preservation of Data-Driven Models in Smart Grids Using Homomorphic Encryption

Dabeeruddin Syed, Shady S. Refaat, Othmane Bouhali
2020 Information  
The proposed methodology is tested for applications of fault identification and localization, and load forecasting in smart grids.  ...  Deep learning models have been applied for varied electrical applications in smart grids with a high degree of reliability and accuracy.  ...  The activation function utilized in the DNN model trained on plain and encrypted data is hyperbolic tangent (tanh) activation.  ... 
doi:10.3390/info11070357 fatcat:n5f7ikqubjclhatawwoa25mnde

A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations [article]

Md. Ashraful Islam, Mir Mahathir Mohammad, Sarkar Snigdha Sarathi Das, Mohammed Eunus Ali
2020 arXiv   pre-print
for a user.  ...  This review can be considered a cookbook for researchers or practitioners working in the area of POI recommendation.  ...  Privacy Preserved POI Recommendation Like many other location-based services, user privacy is a major bottleneck for the proliferation of POI recommendation systems.  ... 
arXiv:2011.10187v1 fatcat:3uampnqerfdvnpuzrxcrsjviwq

The Impact of Differential Privacy on Group Disparity Mitigation [article]

Victor Petrén Bach Hansen, Atula Tejaswi Neerkaje, Ramit Sawhney, Lucie Flek, Anders Søgaard
2022 arXiv   pre-print
Most work in this area has been restricted to computer vision and risk assessment.  ...  The performance cost of differential privacy has, for some applications, been shown to be higher for minority groups; fairness, conversely, has been shown to disproportionally compromise the privacy of  ...  For our smallest text-based dataset (T-US), performance becomes very poor at the strictest privacy level.  ... 
arXiv:2203.02745v1 fatcat:vgfkh3itgvhnffkcacxsqz4owm

2014 Index IEEE Transactions on Parallel and Distributed Systems Vol. 25

2015 IEEE Transactions on Parallel and Distributed Systems  
Verifiable Privacy-Preserving Multi-Keyword Text Search in the Cloud Supporting Similarity-Based Ranking.  ...  ., +, TPDS Nov. 2014 2877-2887 Expressive, Efficient, and Revocable Data Access Control for Multi-Au-Verifiable Privacy-Preserving Multi-Keyword Text Search in the Cloud Supporting Similarity-Based Ranking  ...  ., +, TPDS Aug. 2014 2840 -2850 Energy and Network Aware Workload Management for Sustainable Data Centers with Thermal Storage. 2030 -2042 Hyperbolic Utilization Bounds for Rate Monotonic Scheduling  ... 
doi:10.1109/tpds.2014.2371591 fatcat:qxyljogalrbfficryqjowgv3je

Information Security in Big Data: Privacy and Data Mining

Lei Xu, Chunxiao Jiang, Jian Wang, Jian Yuan, Yong Ren
2014 IEEE Access  
Besides exploring the privacy-preserving approaches for each type of user, we also review the game theoretical approaches, which are proposed for analyzing the interactions among different users in a data  ...  For each type of user, we discuss his privacy concerns and the methods that can be adopted to protect sensitive information.  ...  PERSONALIZED PRIVACY PRESERVING PPDP and PPDM provide methods to explore the utility of data while preserving privacy.  ... 
doi:10.1109/access.2014.2362522 fatcat:oxnmv2kjy5bllhotbkqvxd5rfu

A Unified Review of Deep Learning for Automated Medical Coding [article]

Shaoxiong Ji and Wei Sun and Hang Dong and Honghan Wu and Pekka Marttinen
2022 arXiv   pre-print
., encoder modules for text feature extraction, mechanisms for building deep encoder architectures, decoder modules for transforming hidden representations into medical codes, and the usage of auxiliary  ...  Recent advances in deep learning models in natural language processing have been widely applied to this task.  ...  Dong was supported by Health Data Research UK National Phenomics and Text Analytics Implementation Projects.  ... 
arXiv:2201.02797v1 fatcat:ajl6uq6mkzdo3j2trmfy5ceypq

Representation Learning for Electronic Health Records [article]

Wei-Hung Weng, Peter Szolovits
2019 arXiv   pre-print
Finally, in Section 4 we discuss more techniques, studies, and challenges for learning natural language representations when free texts, such as clinical notes, examination reports, or biomedical literature  ...  In this chapter, we first introduce the background of learning representations and reasons why we need good EHR representations in machine learning for medicine and healthcare in Section 1.  ...  ://github.com/clinicalml/embeddings/ strated that the hyperbolic space embedding preserves the tree structure of the ICD ontology.  ... 
arXiv:1909.09248v1 fatcat:c3yrpp5wanhc7gb5bjpqe4ivbm
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