A Differentially Private Hybrid Approach to Traffic Monitoring

Applied Cryptography and Network Security - Lecture Notes in Computer Science ◽

10.1007/978-3-030-78375-4_10 ◽

2021 ◽

pp. 233-256

Author(s):

Rogério V. M. Rocha ◽

Pedro P. Libório ◽

Harsh Kupwade Patil ◽

Diego F. Aranha

Keyword(s):

Experimental Evaluation ◽

Differential Privacy ◽

Hybrid Approach ◽

Real Data ◽

Communication Technologies ◽

Traffic Monitoring ◽

Vehicular Communication ◽

Sensitive Data ◽

Hybrid Solution ◽

Aggregation Analysis

AbstractIn recent years, privacy research has been gaining ground in vehicular communication technologies. Collecting data from connected vehicles presents a range of opportunities for industry and government to perform data analytics. Although many researchers have explored some privacy solutions for vehicular communications, the conditions to deploy them are still maturing, especially when it comes to privacy for sensitive data aggregation analysis. In this work, we propose a hybrid solution combining the original differential privacy framework with an instance-based additive noise technique. The results show that for typical instances we obtain a significant reduction in outliers. As far as we know, our paper is the first detailed experimental evaluation of differentially private techniques applied to traffic monitoring. The validation of the proposed solution was performed through extensive simulations in typical traffic scenarios using real data.

Assessing the Hybrid Solution

10.1093/oso/9780199560080.003.0013 ◽

2018 ◽

Author(s):

Holly M. Smith

Keyword(s):

Moral Theory ◽

Hybrid Approach ◽

Hybrid Solution

Chapter 13 completes the development and assessment of the Hybrid approach. Final developments involve introducing the concept of an action’s being “decision-mandated,” redefining “subjective obligatoriness” using this concept, and requiring (in order to avoid reemergence of the moral laundry list) that rankings of the guides at all levels be modally robust relative to the governing Code C. Nonetheless some agents may experience forms of sophisticated uncertainty about which act should be chosen according to this Constrained Standards Hybrid approach. In such cases, hopefully rare, there is no subjectively obligatory act and there is no way for the agent to indirectly apply her moral theory. Even so, the Constrained Standards Hybrid approach appears to be the best solution to the problems of error and uncertainty despite the fact that it cannot wholly solve the epistemic limitations that agents may confront.

A Novel Cloud-Assisted Secure Deep Feature Classification Framework for Cancer Histopathology Images

ACM Transactions on Internet Technology ◽

10.1145/3424221 ◽

2021 ◽

Vol 21 (2) ◽

pp. 1-22

Author(s):

Abhinav Kumar ◽

Sanjay Kumar Singh ◽

K Lakshmanan ◽

Sonal Saxena ◽

Sameer Shrivastava

Keyword(s):

Differential Privacy ◽

Model Performance ◽

Healthcare Services ◽

Cloud Services ◽

Efficient Computation ◽

Sensitive Data ◽

Classification Framework ◽

Proposed Model ◽

Diagnostic Framework ◽

Histopathological Image Classification

The advancements in the Internet of Things (IoT) and cloud services have enabled the availability of smart e-healthcare services in a distant and distributed environment. However, this has also raised major privacy and efficiency concerns that need to be addressed. While sharing clinical data across the cloud that often consists of sensitive patient-related information, privacy is a major challenge. Adequate protection of patients’ privacy helps to increase public trust in medical research. Additionally, DL-based models are complex, and in a cloud-based approach, efficient data processing in such models is complicated. To address these challenges, we propose an efficient and secure cancer diagnostic framework for histopathological image classification by utilizing both differential privacy and secure multi-party computation. For efficient computation, instead of performing the whole operation on the cloud, we decouple the layers into two modules: one for feature extraction using the VGGNet module at the user side and the remaining layers for private prediction over the cloud. The efficacy of the framework is validated on two datasets composed of histopathological images of the canine mammary tumor and human breast cancer. The application of differential privacy preserving to the proposed model makes the model secure and capable of preserving the privacy of sensitive data from any adversary, without significantly compromising the model accuracy. Extensive experiments show that the proposed model efficiently achieves the trade-off between privacy and model performance.

RON-Gauss: Enhancing Utility in Non-Interactive Private Data Release

Proceedings on Privacy Enhancing Technologies ◽

10.2478/popets-2019-0003 ◽

2019 ◽

Vol 2019 (1) ◽

pp. 26-46 ◽

Cited By ~ 2

Author(s):

Thee Chanyaswad ◽

Changchang Liu ◽

Prateek Mittal

Keyword(s):

Machine Learning ◽

Real World ◽

Differential Privacy ◽

Real Data ◽

The Novel ◽

Private Data ◽

Data Release ◽

Machine Learning Applications ◽

Order Of Magnitude ◽

Real World Datasets

Abstract A key challenge facing the design of differential privacy in the non-interactive setting is to maintain the utility of the released data. To overcome this challenge, we utilize the Diaconis-Freedman-Meckes (DFM) effect, which states that most projections of high-dimensional data are nearly Gaussian. Hence, we propose the RON-Gauss model that leverages the novel combination of dimensionality reduction via random orthonormal (RON) projection and the Gaussian generative model for synthesizing differentially-private data. We analyze how RON-Gauss benefits from the DFM effect, and present multiple algorithms for a range of machine learning applications, including both unsupervised and supervised learning. Furthermore, we rigorously prove that (a) our algorithms satisfy the strong ɛ-differential privacy guarantee, and (b) RON projection can lower the level of perturbation required for differential privacy. Finally, we illustrate the effectiveness of RON-Gauss under three common machine learning applications – clustering, classification, and regression – on three large real-world datasets. Our empirical results show that (a) RON-Gauss outperforms previous approaches by up to an order of magnitude, and (b) loss in utility compared to the non-private real data is small. Thus, RON-Gauss can serve as a key enabler for real-world deployment of privacy-preserving data release.

Inductive learning and local differential privacy for privacy-preserving offloading in mobile edge intelligent systems

10.36227/techrxiv.13698883 ◽

2021 ◽

Author(s):

Jude TCHAYE-KONDI ◽

Yanlong Zhai ◽

Liehuang Zhu

Keyword(s):

Intelligent Systems ◽

Differential Privacy ◽

Inductive Learning ◽

Random Noise ◽

Privacy Preserving ◽

Sensitive Data ◽

Privacy Concerns ◽

Feature Extractor ◽

Data Source ◽

Series Of Experiments

<div>We address privacy and latency issues in the edge/cloud computing environment while training a centralized AI model. In our particular case, the edge devices are the only data source for the model to train on the central server. Current privacy-preserving and reducing network latency solutions rely on a pre-trained feature extractor deployed on the devices to help extract only important features from the sensitive dataset. However, finding a pre-trained model or pubic dataset to build a feature extractor for certain tasks may turn out to be very challenging. With the large amount of data generated by edge devices, the edge environment does not really lack data, but its improper access may lead to privacy concerns. In this paper, we present DeepGuess , a new privacy-preserving, and latency aware deeplearning framework. DeepGuess uses a new learning mechanism enabled by the AutoEncoder(AE) architecture called Inductive Learning, which makes it possible to train a central neural network using the data produced by end-devices while preserving their privacy. With inductive learning, sensitive data remains on devices and is not explicitly involved in any backpropagation process. The AE’s Encoder is deployed on devices to extracts and transfers important features to the server. To enhance privacy, we propose a new local deferentially private algorithm that allows the Edge devices to apply random noise to features extracted from their sensitive data before transferred to an untrusted server. The experimental evaluation of DeepGuess demonstrates its effectiveness and ability to converge on a series of experiments.</div>

The issues connected with the anonymization of medical data. Part 2. Advanced anonymization and anonymization controlled by owner of protected sensitive data

HIGHER SCHOOL’S PULSE ◽

10.5604/01.3001.0003.3161 ◽

2014 ◽

Vol 8 (2) ◽

pp. 13-24 ◽

Cited By ~ 1

Author(s):

Arkadiusz Liber

Keyword(s):

Privacy Protection ◽

Differential Privacy ◽

Data Access ◽

Medical Data ◽

Medical Documentation ◽

Research Review ◽

Sensitive Data ◽

Data Anonymization ◽

Data Access Control ◽

Anonymized Data

Introduction: Medical documentation ought to be accessible with the preservation of its integrity as well as the protection of personal data. One of the manners of its protection against disclosure is anonymization. Contemporary methods ensure anonymity without the possibility of sensitive data access control. it seems that the future of sensitive data processing systems belongs to the personalized method. In the first part of the paper k-Anonymity, (X,y)- Anonymity, (α,k)- Anonymity, and (k,e)-Anonymity methods were discussed. these methods belong to well - known elementary methods which are the subject of a significant number of publications. As the source papers to this part, Samarati, Sweeney, wang, wong and zhang’s works were accredited. the selection of these publications is justified by their wider research review work led, for instance, by Fung, Wang, Fu and y. however, it should be noted that the methods of anonymization derive from the methods of statistical databases protection from the 70s of 20th century. Due to the interrelated content and literature references the first and the second part of this article constitute the integral whole.Aim of the study: The analysis of the methods of anonymization, the analysis of the methods of protection of anonymized data, the study of a new security type of privacy enabling device to control disclosing sensitive data by the entity which this data concerns.Material and methods: Analytical methods, algebraic methods.Results: Delivering material supporting the choice and analysis of the ways of anonymization of medical data, developing a new privacy protection solution enabling the control of sensitive data by entities which this data concerns.Conclusions: In the paper the analysis of solutions for data anonymization, to ensure privacy protection in medical data sets, was conducted. the methods of: k-Anonymity, (X,y)- Anonymity, (α,k)- Anonymity, (k,e)-Anonymity, (X,y)-Privacy, lKc-Privacy, l-Diversity, (X,y)-linkability, t-closeness, confidence Bounding and Personalized Privacy were described, explained and analyzed. The analysis of solutions of controlling sensitive data by their owner was also conducted. Apart from the existing methods of the anonymization, the analysis of methods of the protection of anonymized data was included. In particular, the methods of: δ-Presence, e-Differential Privacy, (d,γ)-Privacy, (α,β)-Distributing Privacy and protections against (c,t)-isolation were analyzed. Moreover, the author introduced a new solution of the controlled protection of privacy. the solution is based on marking a protected field and the multi-key encryption of sensitive value. The suggested way of marking the fields is in accordance with Xmlstandard. For the encryption, (n,p) different keys cipher was selected. to decipher the content the p keys of n were used. The proposed solution enables to apply brand new methods to control privacy of disclosing sensitive data.

Application of Advanced Communication Technologies in the Improvement of Traffic Safety

JTTTP - JOURNAL OF TRAFFIC AND TRANSPORT THEORY AND PRACTICE ◽

10.7251/jtttp1701021m ◽

2017 ◽

Vol 2 (1-2) ◽

Author(s):

Goran Z. Marković

Keyword(s):

Traffic Safety ◽

Communication Systems ◽

Communication Technologies ◽

Vehicular Communication ◽

Future Directions ◽

Car Industry ◽

Dedicated Short Range Communications ◽

Traffic Safety Applications ◽

Safety Applications

Incorporation of advanced info-communication technologies into vehicular environment currently captures a large attention by numerous investigators, telecommunications operators, traffic safety regulatory institutions, car industry manufacturers and other interested participants. In this paper, we overview of some prospective wireless communication technologies, such as the DSRC (Dedicated Short Range Communications) and advanced LTE (Long Term Evolution) mobile communication systems, which are considered as two promising candidates to support future traffic safety applications in vehicular environment is presented. The communication requirements of some active traffic safety applications are pointed. A summary of various types of communications for intelligent VCS (Vehicular Communication System) applications is given. Some future directions and challenging issues for implementing traffic safety applications are also discussed. Our goal is to demonstrate the growing impact and importance of modern communication technologies in achieving future traffic accident-free roads.

Differential privacy for dynamical sensitive data

2017 IEEE 56th Annual Conference on Decision and Control (CDC) ◽

10.1109/cdc.2017.8263806 ◽

2017 ◽

Cited By ~ 4

Author(s):

Fragkiskos Koufogiannis ◽

George J. Pappas

Keyword(s):

Differential Privacy ◽

Sensitive Data

Differentially Private Image Classification Using Support Vector Machine and Differential Privacy

Machine Learning and Knowledge Extraction ◽

10.3390/make1010029 ◽

2019 ◽

Vol 1 (1) ◽

pp. 483-491 ◽

Cited By ~ 6

Author(s):

Makhamisa Senekane

Keyword(s):

Support Vector Machine ◽

Data Analysis ◽

Image Classification ◽

Differential Privacy ◽

Privacy Preserving ◽

Global Optimum ◽

Support Vector ◽

Sensitive Data ◽

Radiological Images ◽

Golden Standard

The ubiquity of data, including multi-media data such as images, enables easy mining and analysis of such data. However, such an analysis might involve the use of sensitive data such as medical records (including radiological images) and financial records. Privacy-preserving machine learning is an approach that is aimed at the analysis of such data in such a way that privacy is not compromised. There are various privacy-preserving data analysis approaches such as k-anonymity, l-diversity, t-closeness and Differential Privacy (DP). Currently, DP is a golden standard of privacy-preserving data analysis due to its robustness against background knowledge attacks. In this paper, we report a scheme for privacy-preserving image classification using Support Vector Machine (SVM) and DP. SVM is chosen as a classification algorithm because unlike variants of artificial neural networks, it converges to a global optimum. SVM kernels used are linear and Radial Basis Function (RBF), while ϵ -differential privacy was the DP framework used. The proposed scheme achieved an accuracy of up to 98%. The results obtained underline the utility of using SVM and DP for privacy-preserving image classification.

Privacy Preserving Machine Learning and Deep Learning Techniques

Handbook of Research on Applications and Implementations of Machine Learning Techniques - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-9902-9.ch012 ◽

2020 ◽

pp. 222-235

Author(s):

Divya Asok ◽

Chitra P. ◽

Bharathiraja Muthurajan

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Data Privacy ◽

Differential Privacy ◽

Digital Data ◽

Sensitive Data ◽

Security Issues ◽

Private Data ◽

Learning Techniques ◽

Machine Learning Applications

In the past years, the usage of internet and quantity of digital data generated by large organizations, firms, and governments have paved the way for the researchers to focus on security issues of private data. This collected data is usually related to a definite necessity. For example, in the medical field, health record systems are used for the exchange of medical data. In addition to services based on users' current location, many potential services rely on users' location history or their spatial-temporal provenance. However, most of the collected data contain data identifying individual which is sensitive. With the increase of machine learning applications around every corner of the society, it could significantly contribute to the preservation of privacy of both individuals and institutions. This chapter gives a wider perspective on the current literature on privacy ML and deep learning techniques, along with the non-cryptographic differential privacy approach for ensuring sensitive data privacy.

Wheelchair Neuro Fuzzy Control and Tracking System Based on Voice Recognition

Sensors ◽

10.3390/s20102872 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2872 ◽

Cited By ~ 2

Author(s):

Mokhles M. Abdulghani ◽

Kasim M. Al-Aubidy ◽

Mohammed M. Ali ◽

Qadri J. Hamarsheh

Keyword(s):

Fuzzy Controller ◽

Tracking System ◽

Voice Recognition ◽

Real Data ◽

Communication Technologies ◽

Real Time Control ◽

Time Control ◽

Smart Wheelchairs ◽

Neuro Fuzzy ◽

The Disabled

Autonomous wheelchairs are important tools to enhance the mobility of people with disabilities. Advances in computer and wireless communication technologies have contributed to the provision of smart wheelchairs to suit the needs of the disabled person. This research paper presents the design and implementation of a voice controlled electric wheelchair. This design is based on voice recognition algorithms to classify the required commands to drive the wheelchair. An adaptive neuro-fuzzy controller has been used to generate the required real-time control signals for actuating motors of the wheelchair. This controller depends on real data received from obstacle avoidance sensors and a voice recognition classifier. The wheelchair is considered as a node in a wireless sensor network in order to track the position of the wheelchair and for supervisory control. The simulated and running experiments demonstrate that, by combining the concepts of soft-computing and mechatronics, the implemented wheelchair has become more sophisticated and gives people more mobility.