scholarly journals Differential Privacy Protection of Face Images Based on Region Growing

2021 ◽  
Vol 38 (5) ◽  
pp. 1385-1401
Author(s):  
Chao Liu ◽  
Jing Yang ◽  
Weinan Zhao ◽  
Yining Zhang ◽  
Cuiping Shi ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Differential Privacy ◽  
Region Growing ◽  
Sensitive Information ◽  
Contrast Structure ◽  
Seed Point ◽  
Face Images ◽  
Measurement Mechanism ◽  
Privacy Budget ◽  
Denoised Image

Face images, as an information carrier, are rich in sensitive information. Direct publication of these images would cause privacy leak, due to their natural weak privacy. Most of the existing privacy protection methods for face images adopt data publication under a non-interactive framework. However, the E-effect under this framework covers the entire image, such that the noise influence is uniform across the image. To solve the problem, this paper proposes region growing publication (RGP), an algorithm for the interactive publication of face images under differential privacy. This innovative algorithm combines the region growing technique with differential privacy technique. The privacy budget E is dynamically allocated, and the Laplace noise is added, according to the similarity between adjacent sub-images. To measure this similarity more effectively, the fusion similarity measurement mechanism (FSMM) was designed, which better adapts to the intrinsic attributes of images. Different from traditional region growing rules, the FSMM fully considers various attributes of images, including brightness, contrast, structure, color, texture, and spatial distribution. To further enhance algorithm feasibility, RGP was extended to atypical region growing publication (ARGP). While RGP limits the region growing direction between adjacent sub-images, ARGP searches for the qualified sub-images across the image, with the aid of the exponential mechanism, thereby expanding the region merging scope of the seed point. The results show that our algorithm can satisfy E-differential privacy, and the denoised image still have a high availability.

scholarly journals Face Image Publication Based on Differential Privacy

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Chao Liu ◽  
Jing Yang ◽  
Weinan Zhao ◽  
Yining Zhang ◽  
Jingyou Li ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Data Stream ◽  
Differential Privacy ◽  
Sliding Window ◽  
Data Encryption ◽  
Face Image ◽  
Dynamic Allocation ◽  
Face Images ◽  
Privacy Budget ◽  
The Impact

As an information carrier, face images contain abundant sensitive information. Due to its natural weak privacy, direct publishing may divulge privacy. Anonymization Technology and Data Encryption Technology are limited by the background knowledge and attack means of attackers, which cannot completely content the needs of face image privacy protection. Therefore, this paper proposes a face image publishing SWP (sliding window publication) algorithm, which satisfies the differential privacy. Firstly, the SWP translates the image gray matrix into a one-dimensional ordered data stream by using image segmentation technology. The purpose of this step is to transform the image privacy protection problem into the data stream privacy protection problem. Then, the sliding window model is used to model the data flow. By comparing the similarity of data in adjacent sliding windows, the privacy budget is dynamically allocated, and Laplace noise is added. In SWP, the data in the sliding window comes from the image. To present the image features contained in the data more comprehensively and use the privacy budget more reasonably, this paper proposes a fusion similarity measurement EM (exact mechanism) mechanism and a dynamic privacy budget allocation DA (dynamic allocation) mechanism. Also, for further improving the usability of human face images and reducing the impact of noise, a sort-SWP algorithm based on the SWP method is proposed in the paper. Through the analysis, it can be seen that ordered input can further improve the usability of the SWP algorithm, but direct sorting of data will destroy the ε -differential privacy. Therefore, this paper proposes a sorting method-SAS method, which satisfies the ε -differential privacy; SAS obtain an initial sort by using an exponential mechanism firstly. And then an approximate correct sort is obtained by using the Annealing algorithm to optimize the initial sort. Compared with LAP algorithm and SWP algorithm, the average accuracy rate of sort-SWP algorithm in ORL, Yale is increased by 56.63% and 21.55%, the recall rate is increased by 6.85% and 3.32%, and F1-sroce is improved by 55.62% and 16.55%.

scholarly journals Learning With Differential Privacy

2021 ◽  
pp. 372-395
Author(s):  
Poushali Sengupta ◽  
Sudipta Paul ◽  
Subhankar Mishra
Keyword(s):  
Privacy Protection ◽  
Differential Privacy ◽  
Intrusion Detection Systems ◽  
Sensitive Information ◽  
Detection Systems ◽  
Trade Offs ◽  
Personal Level ◽  
Encryption Decryption ◽  
Individual Trees ◽  
Prevention Methods

The leakage of data might have an extreme effect on the personal level if it contains sensitive information. Common prevention methods like encryption-decryption, endpoint protection, intrusion detection systems are prone to leakage. Differential privacy comes to the rescue with a proper promise of protection against leakage, as it uses a randomized response technique at the time of collection of the data which promises strong privacy with better utility. Differential privacy allows one to access the forest of data by describing their pattern of groups without disclosing any individual trees. The current adaption of differential privacy by leading tech companies and academia encourages authors to explore the topic in detail. The different aspects of differential privacy, its application in privacy protection and leakage of information, a comparative discussion on the current research approaches in this field, its utility in the real world as well as the trade-offs will be discussed.

scholarly journals Preserving Differential Privacy for Similarity Measurement in Smart Environments

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Kok-Seng Wong ◽  
Myung Ho Kim
Keyword(s):  
Privacy Protection ◽  
Differential Privacy ◽  
Smart Environments ◽  
Coefficient Function ◽  
Sensitive Information ◽  
Smart Environment ◽  
Privacy Concerns ◽  
Privacy Model ◽  
The Subject ◽  
Measurement Metric

Advances in both sensor technologies and network infrastructures have encouraged the development of smart environments to enhance people’s life and living styles. However, collecting and storing user’s data in the smart environments pose severe privacy concerns because these data may contain sensitive information about the subject. Hence, privacy protection is now an emerging issue that we need to consider especially when data sharing is essential for analysis purpose. In this paper, we consider the case where two agents in the smart environment want to measure the similarity of their collected or stored data. We use similarity coefficient functionFSCas the measurement metric for the comparison with differential privacy model. Unlike the existing solutions, our protocol can facilitate more than one request to computeFSCwithout modifying the protocol. Our solution ensures privacy protection for both the inputs and the computedFSCresults.

scholarly journals Non-Global Privacy Protection Facing Sensitive Areas in Face Images

2021 ◽  
Vol 38 (6) ◽  
pp. 1677-1687
Author(s):  
Chao Liu ◽  
Jing Yang ◽  
Yining Zhang ◽  
Xuan Zhang ◽  
Weinan Zhao ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Differential Privacy ◽  
Total Error ◽  
Regional Growth ◽  
Sensitive Area ◽  
Image Size ◽  
Personal Privacy ◽  
Sensitive Areas ◽  
Face Images ◽  
Protection Method

Face images, as an information carrier, are naturally weak in privacy. If they are collected and analyzed by malicious third parties, personal privacy will leak, and many other unmeasurable losses will occur. Differential privacy protection of face images is mainly being studied under non-interactive frameworks. However, the ε-effect impacts the entire image under these frameworks. Besides, the noise influence is uniform across the protected image, during the realization of the Laplace mechanism. The differential privacy of face images under interactive mechanisms can protect the privacy of different areas to different degrees, but the total error is still constrained by the image size. To solve the problem, this paper proposes a non-global privacy protection method for sensitive areas in face images, known as differential privacy of landmark positioning (DPLP). The proposed algorithm is realized as follows: Firstly, the active shape model (ASM) algorithm was adopted to position the area of each face landmark. If the landmark overlaps a subgraph of the original image, then the subgraph would be taken as a sensitive area. Then, the sensitive area was treated as the seed for regional growth, following the fusion similarity measurement mechanism (FSMM). In our method, the privacy budget is only allocated to the seed; whether any other insensitive area would be protected depends on whether the area exists in a growing region. In addition, when a subgraph meets the criterion for merging with multiple seeds, the most reasonable seed to be merged would be selected by the exponential mechanism. Experimental results show that the DPLP algorithm satisfies ε-differential privacy, its total error does not change with image size, and the noisy image remains highly available.

scholarly journals The issues connected with the anonymization of medical data. Part 1. The introduction to the anonymization of medical data. Ensuring the protection of sensitive information with the use of such methods as f(a) and f(a,b)

2014 ◽  
Vol 8 (1) ◽  
pp. 13-21 ◽  
Author(s):  
ARKADIUSZ LIBER
Keyword(s):  
Privacy Protection ◽  
Data Privacy ◽  
Differential Privacy ◽  
Medical Data ◽  
Medical Documentation ◽  
Sensitive Information ◽  
Sensitive Data ◽  
New Methods ◽  
Anonymized Data ◽  
Data Privacy Protection

Introduction: Medical documentation must be protected against damage or loss, in compliance with its integrity and credibility and the opportunity to a permanent access by the authorized staff and, finally, protected against the access of unauthorized persons. Anonymization is one of the methods to safeguard the data against the disclosure.Aim of the study: The study aims at the analysis of methods of anonymization, the analysis of methods of the protection of anonymized data and the study of a new security type of privacy enabling to control sensitive data by the entity which the data concerns.Material and methods: The analytical and algebraic methods were used.Results: The study ought to deliver the materials supporting the choice and analysis of the ways of the anonymization of medical data, and develop a new privacy protection solution enabling the control of sensitive data by entities whom this data concerns.Conclusions: In the paper, the analysis of solutions of data anonymizing used for medical data privacy protection was con-ducted. The methods, such as k-Anonymity, (X,y)- Anonymity, (a,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 to control sensitive data by their owners was also conducted. Apart from the existing methods of the anonymization, the analysis of methods of the anonimized data protection was conducted, in particular the methods of: d-Presence, e-Differential Privacy, (d,g)-Privacy, (a,b)-Distributing Privacy and protections against (c,t)-Isolation were analyzed. The author introduced a new solution of the controlled protection of privacy. The solution is based on marking a protected field and multi-key encryption of the sensitive value. The suggested way of fields marking is in accordance to the XML standard. For the encryption (n,p) different key cipher was selected. To decipher the content the p keys of n is used. The proposed solution enables to apply brand new methods for the control of privacy of disclosing sensitive data.

scholarly journals Differential Privacy Preserving in Big data Analytics for Body Area Networks

2021 ◽  
Vol 9 (12) ◽  
pp. 514-518
Author(s):  
Adam Gowri Shankar
Keyword(s):  
Big Data ◽  
Privacy Protection ◽  
Differential Privacy ◽  
Wearable Sensors ◽  
Big Data Analytics ◽  
Body Area Networks ◽  
Area Network ◽  
Sensitive Information ◽  
Body Area ◽  
Protection Scheme

Abstract: Body Area Networks (BANs), collects enormous data by wearable sensors which contain sensitive information such as physical condition, location information, and so on, which needs protection. Preservation of privacy in big data has emerged as an absolute prerequisite for exchanging private data in terms of data analysis, validation, and publishing. Previous methods and traditional methods like k-anonymity and other anonymization techniques have overlooked privacy protection issues resulting to privacy infringement. In this work, a differential privacy protection scheme for ‘big data in body area network’ is developed. Compared with previous methods, the proposed privacy protection scheme is best in terms of availability and reliability. Exploratory results demonstrate that, even when the attacker has full background knowledge, the proposed scheme can still provide enough interference to big sensitive data so as to preserve the privacy. Keywords: BAN’s, Privacy, Differential Privacy, Noisy response

scholarly journals A Movie Recommendation System Based on Differential Privacy Protection

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Min Li ◽  
Yingming Zeng ◽  
Yue Guo ◽  
Yun Guo
Keyword(s):  
Privacy Protection ◽  
Recommendation System ◽  
Differential Privacy ◽  
Recommendation Systems ◽  
Current Recommendation ◽  
Protection Scheme ◽  
The Past ◽  
Collaborative Filtering Algorithm ◽  
Movie Recommendation ◽  
Privacy Budget

In the past decades, the ever-increasing popularity of the Internet has led to an explosive growth of information, which has consequently led to the emergence of recommendation systems. A series of cloud-based encryption measures have been adopted in the current recommendation systems to protect users’ privacy. However, there are still many other privacy attacks on the local devices. Therefore, this paper studies the encryption interference of applying a differential privacy protection scheme on the data in the user’s local devices under the assumption of an untrusted server. A dynamic privacy budget allocation method is proposed based on a localized differential privacy protection scheme while taking the specific application scene of movie recommendation into consideration. What is more, an improved user-based collaborative filtering algorithm, which adopts a matrix-based similarity calculation method instead of the traditional vector-based method when computing the user similarity, is proposed. Finally, it was proved by experimental results that the differential privacy-based movie recommendation system (DP-MRE) proposed in this paper could not only protect the privacy of users but also ensure the accuracy of recommendations.

scholarly journals Location recommendation privacy protection method based on location sensitivity division

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Chunyong Yin ◽  
Xiaokang Ju ◽  
Zhichao Yin ◽  
Jin Wang
Keyword(s):  
Service Quality ◽  
Privacy Protection ◽  
Location Privacy ◽  
Differential Privacy ◽  
Experimental Results ◽  
Location Information ◽  
Location Recommendation ◽  
Protection Method ◽  
Privacy Budget ◽  
The Impact

AbstractLocation-based recommendation services can provide users with convenient services, but this requires monitoring and collecting a large amount of location information. In order to prevent location information from being leaked after monitoring and collection, location privacy must be effectively protected. Therefore, this paper proposes a privacy protection method based on location sensitivity for location recommendation. This method uses location trajectories and check-in frequencies to set a threshold so as to classify location sensitivity levels. The corresponding privacy budget is then assigned based on the sensitivity to add Laplace noise that satisfies the differential privacy. Experimental results show that this method can effectively protect the user’s location privacy and reduce the impact of differential privacy noise on service quality.

Order Dataset Release Scheme Based on Safe K-Anonymization for Privacy Protection in Cloud Manufacturing

2017 ◽  
Author(s):  
Hui Xiu ◽  
Xuemei Jiang ◽  
Xiaomei Zhang
Keyword(s):  
Privacy Protection ◽  
Traditional Method ◽  
Differential Privacy ◽  
Cloud Manufacturing ◽  
Statistical Characteristics ◽  
Sensitive Information ◽  
Improved Method ◽  
Original Order ◽  
Privacy Leakage ◽  
Manufacturing Resources

Cloud Manufacturing is a new model to increase the manufacturing and business benefits by sharing manufacturing resources. These resources can bring users convenience, but also may be maliciously analyzed by the attacker which may result in personal or corporate privacy disclosure. In this paper, we discuss the privacy disclosure problem in cloud manufacturing, and propose a method for releasing order data securely with the complex relationship between enterprises and other vendors. With regards to the risk of privacy leakage in the process of data analysis or data mining, we improve the traditional method of anonymous releasing for original order data, and introduce the thought of safe k-anonymization to achieve the process. To meet the needs of protecting sensitive information in data, we analyze the users’ different demands for order data in the cloud manufacturing, use the sampling function to satisfy (β, ε, δ) - DPS to increase the uncertainty of the differential privacy, improve the k-anonymization method, apply the anonymous method with generalization, concealment, and reduce data associations to different attributes. The improved method not only preserves the statistical characteristics of the data, but also protects the privacy information in the order data in the cloud manufacturing environment.

scholarly journals A smoke image segmentation algorithm based on rough set and region growing

2019 ◽  
Vol 65 (No. 8) ◽  
pp. 321-329
Author(s):  
Haitao Wang ◽  
Yanli Chen
Keyword(s):  
Image Segmentation ◽  
Rough Set ◽  
Region Growing ◽  
Recall Rate ◽  
Segmentation Algorithm ◽  
Colour Space ◽  
Seed Point ◽  
Black Smoke ◽  
Growth Method ◽  
Image Segmentation Algorithm

Because the image fire smoke segmentation algorithm can not extract white, gray and black smoke at the same time, a smoke image segmentation algorithm is proposed by combining rough set and region growth method. The R component of the image is extracted in the RGB colour space, the roughness histogram is constructed according to the statistical histogram of the R component, and the appropriate valley value in the roughness histogram is selected as the segmentation threshold, the image is roughly segmented. Relative to the background image, the smoke belongs to the motion information, and the motion region is extracted by the interframe difference method to eliminate static interference. Smoke has a unique colour feature, a smoke colour model is created in the RGB colour space, the motion disturbances of similar colour are removed and the suspected smoke areas are obtained. The seed point is selected in the region, and the region is grown on the result of rough segmentation, the smoke region is extracted. The experimental results show that the algorithm can segment white, gray and black smoke at the same time, and the irregular information of smoke edges is relatively complete. Compared with the existing algorithms, the average segmentation accuracy, recall rate and F-value are increased by 19%, 21.5% and 20%, respectively.<br /><br />

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