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 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.

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 Differentially Private Autocorrelation Time-Series Data Publishing Based on Sliding Window

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jing Zhao ◽  
Shubo Liu ◽  
Xingxing Xiong ◽  
Zhaohui Cai
Keyword(s):  
Time Series ◽  
Privacy Protection ◽  
Large Scale ◽  
Differential Privacy ◽  
Time Series Data ◽  
Sliding Window ◽  
Data Publishing ◽  
Series Data ◽  
Data Publication ◽  
Autocorrelation Time

Privacy protection is one of the major obstacles for data sharing. Time-series data have the characteristics of autocorrelation, continuity, and large scale. Current research on time-series data publication mainly ignores the correlation of time-series data and the lack of privacy protection. In this paper, we study the problem of correlated time-series data publication and propose a sliding window-based autocorrelation time-series data publication algorithm, called SW-ATS. Instead of using global sensitivity in the traditional differential privacy mechanisms, we proposed periodic sensitivity to provide a stronger degree of privacy guarantee. SW-ATS introduces a sliding window mechanism, with the correlation between the noise-adding sequence and the original time-series data guaranteed by sequence indistinguishability, to protect the privacy of the latest data. We prove that SW-ATS satisfies ε-differential privacy. Compared with the state-of-the-art algorithm, SW-ATS is superior in reducing the error rate of MAE which is about 25%, improving the utility of data, and providing stronger privacy protection.

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 Impact of Asymmetric Left and Asymmetric Right Face Images on Accurate Age Estimation

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Muhammad Sajid ◽  
Naeem Iqbal Ratyal ◽  
Nouman Ali ◽  
Bushra Zafar ◽  
Saadat Hanif Dar ◽  
...  
Keyword(s):  
Age Estimation ◽  
State Of The Art ◽  
Face Image ◽  
Facial Aging ◽  
Face Images ◽  
Left And Right ◽  
Sleeping Habits ◽  
The Impact ◽  
Asymmetric Aging ◽  
Sun Light

Aging affects left and right half face differently owing to numerous factors such as sleeping habits, exposure to sun light, and weaker face muscles of one side of face. In computer vision, age of a given face image is estimated using features that are correlated with age, such as moles, scars, and wrinkles. In this study we report the asymmetric aging of the left and right sides of face images and its impact on accurate age estimation. Left symmetric faces were perceived as younger while right symmetric faces were perceived as older when presented to the state-of-the-art age estimator. These findings show that facial aging is an asymmetric process which plays role in accurate facial age estimation. Experimental results on two large datasets verify the significance of using asymmetric right face image to estimate the age of a query face image more accurately compared to the corresponding original or left asymmetric face image.

scholarly journals Utility Optimization of Federated Learning with Differential Privacy

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Jianzhe Zhao ◽  
Keming Mao ◽  
Chenxi Huang ◽  
Yuyang Zeng
Keyword(s):  
Differential Privacy ◽  
Learning Algorithm ◽  
Wasserstein Distance ◽  
Dynamic Allocation ◽  
Model Accuracy ◽  
Learning Framework ◽  
Utility Optimization ◽  
Data Heterogeneity ◽  
Cross Platform ◽  
The Impact

Secure and trusted cross-platform knowledge sharing is significant for modern intelligent data analysis. To address the trade-off problems between privacy and utility in complex federated learning, a novel differentially private federated learning framework is proposed. First, the impact of data heterogeneity of participants on global model accuracy is analyzed quantitatively based on 1-Wasserstein distance. Then, we design a multilevel and multiparticipant dynamic allocation method of privacy budget to reduce the injected noise, and the utility can be improved efficiently. Finally, they are integrated, and a novel adaptive differentially private federated learning algorithm (A-DPFL) is designed. Comprehensive experiments on redefined non-I.I.D MNIST and CIFAR-10 datasets are conducted, and the results demonstrate the superiority of model accuracy, convergence, and robustness.

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 Transferable face image privacy protection based on federated learning and ensemble models

2021 ◽  
Author(s):  
Jingjing Yang ◽  
Jiaxing Liu ◽  
Runkai Han ◽  
Jinzhao Wu
Keyword(s):  
Privacy Protection ◽  
Facial Recognition ◽  
Image Features ◽  
Face Image ◽  
Recognition Model ◽  
Ensemble Models ◽  
Face Images ◽  
Parameter Matrix ◽  
Differential Evolutionary Algorithm ◽  
Global Optimal

AbstractFace image features represent significant user privacy concerns. Face images cannot be privately transferred under existing privacy protection methods, and data across various social networks are unevenly distributed. This paper proposes a method for face image privacy protection based on federated learning and ensemble models. A federated learning model based on distributed data sets was established by means of federated learning. On the client side, a local facial recognition model was obtained by local face data training and used as the input of PcadvGAN to train PcadvGAN for several rounds. On the server side, a parameter aggregator based on a differential evolutionary algorithm was established as the discriminator of PcadvGAN server, and a client facial recognition model was ensembled simultaneously. The discriminator of the PcadvGAN server experienced mutation, crossover, and interaction with the ensemble model to reveal the optimal global weight of the PcadvGAN model. Finally, the global optimal aggregation parameter matrix of PcadvGAN was obtained by calculation. The server and the client shared the global optimal aggregation parameter matrix, enabling each client to generate private face images with high transferability and practicality. Targeted attack and non-targeted attack experiments demonstrated that the proposed method can generate high-quality, transferable, robust, private face images with only minor perturbations more effectively than other existing methods.

scholarly journals Impact of Frequency of Location Reports on the Privacy Level of Geo-indistinguishability

2020 ◽  
Vol 2020 (2) ◽  
pp. 379-396 ◽  
Author(s):  
Ricardo Mendes ◽  
Mariana Cunha ◽  
João P. Vilela
Keyword(s):  
Location Privacy ◽  
Differential Privacy ◽  
Location Based Services ◽  
Potential Threat ◽  
Mobility Data ◽  
Research Gap ◽  
Privacy Budget ◽  
The Impact ◽  
User Location ◽  
Privacy Level

AbstractLocation privacy has became an emerging topic due to the pervasiveness of Location-Based Services (LBSs). When sharing location, a certain degree of privacy can be achieved through the use of Location Privacy-Preserving Mechanisms (LPPMs), in where an obfuscated version of the exact user location is reported instead. However, even obfuscated location reports disclose information which poses a risk to privacy. Based on the formal notion of differential privacy, Geo-indistinguishability has been proposed to design LPPMs that limit the amount of information that is disclosed to a potential adversary observing the reports. While promising, this notion considers reports to be independent from each other, thus discarding the potential threat that arises from exploring the correlation between reports. This assumption might hold for the sporadic release of data, however, there is still no formal nor quantitative boundary between sporadic and continuous reports and thus we argue that the consideration of independence is valid depending on the frequency of reports made by the user. This work intends to fill this research gap through a quantitative evaluation of the impact on the privacy level of Geo-indistinguishability under different frequency of reports. Towards this end, state-of-the-art localization attacks and a tracking attack are implemented against a Geo-indistinguishable LPPM under several values of privacy budget and the privacy level is measured along different frequencies of updates using real mobility data.

scholarly journals A New Mining and Protection Method Based on Sensitive Data

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Xiaoyao Zheng ◽  
Yuqing Liu ◽  
Hao You ◽  
Liangmin Guo ◽  
Chuanxin Zhao
Keyword(s):  
Sensitivity Analysis ◽  
Traditional Method ◽  
Data Stream ◽  
Data Flow ◽  
Sliding Window ◽  
Sensitive Data ◽  
Time Threshold ◽  
Protection Method ◽  
Data Value ◽  
The Impact

The traditional method of sensitive data identification for data stream has a large amount of calculation and does not reflect the impact of time on the data value, and the mining accuracy is not high. In view of the above problems we firstly adopt the sliding window mechanism to divide the data flow according to time and delay the dataset according to the characteristics of the data flow in the sliding window to achieve the purpose of saving time and space. At the same time, threshold sensitivity analysis is used to find out the optimal threshold. Finally, a K-anonymous algorithm based on dynamic rounding function is employed to achieve the protection of sensitive data. Theoretical analysis and experimental results show that the algorithm can effectively mine the sensitive data in the data stream and can effectively protect the sensitive data.

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