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Signal Processing and Cryptography

Image Analysis and Processing in the Encrypted Domain


In this research project, we are interested by finding solutions to the problem of image analysis and processing in the encrypted domain. For security reasons, more and more digital data are transferred or stored in the encrypted domain. However, during the transmission or the archiving of encrypted images, it is often necessary to analyze or process them, without knowing the original content or the secret key used during the encryption phase. We propose to work on this problem, by associating theoretical aspects with numerous applications.

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20 September 2019 - 12:16pm
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20190912_Poster_3MT_ICIP2019_PPuteaux.pdf

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[1] , "Image Analysis and Processing in the Encrypted Domain", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4792. Accessed: Sep. 21, 2019.
@article{4792-19,
url = {http://sigport.org/4792},
author = { },
publisher = {IEEE SigPort},
title = {Image Analysis and Processing in the Encrypted Domain},
year = {2019} }
TY - EJOUR
T1 - Image Analysis and Processing in the Encrypted Domain
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4792
ER -
. (2019). Image Analysis and Processing in the Encrypted Domain. IEEE SigPort. http://sigport.org/4792
, 2019. Image Analysis and Processing in the Encrypted Domain. Available at: http://sigport.org/4792.
. (2019). "Image Analysis and Processing in the Encrypted Domain." Web.
1. . Image Analysis and Processing in the Encrypted Domain [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4792

Privacy Protection for Social Media based on a Hierarchical Secret Image Sharing Scheme


Social network development raises many issues relating to privacy protection for images. In particular, multi-party privacy protection conflicts can take place when an image is published by only one of its owners. Indeed, privacy settings applied to this image are those of its owner and people on the image are not involved in the process. In this paper, we propose a new hierarchical secret image sharing scheme for social networks in order to answer this problem.

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20 September 2019 - 11:46am
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[1] , "Privacy Protection for Social Media based on a Hierarchical Secret Image Sharing Scheme", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4790. Accessed: Sep. 21, 2019.
@article{4790-19,
url = {http://sigport.org/4790},
author = { },
publisher = {IEEE SigPort},
title = {Privacy Protection for Social Media based on a Hierarchical Secret Image Sharing Scheme},
year = {2019} }
TY - EJOUR
T1 - Privacy Protection for Social Media based on a Hierarchical Secret Image Sharing Scheme
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4790
ER -
. (2019). Privacy Protection for Social Media based on a Hierarchical Secret Image Sharing Scheme. IEEE SigPort. http://sigport.org/4790
, 2019. Privacy Protection for Social Media based on a Hierarchical Secret Image Sharing Scheme. Available at: http://sigport.org/4790.
. (2019). "Privacy Protection for Social Media based on a Hierarchical Secret Image Sharing Scheme." Web.
1. . Privacy Protection for Social Media based on a Hierarchical Secret Image Sharing Scheme [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4790

Chained Compressed Sensing for IoT Node Security


Compressed sensing can be used to yield both compression and a limited form of security to the readings of sensors. This can be most useful when designing the low-resources sensor nodes that are the backbone of IoT applications. Here, we propose to use chaining of subsequent plaintexts to improve the robustness of CS-based encryption against ciphertext-only attacks, known-plaintext attacks and man-in-the-middle attacks.

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Authors:
Mauro Mangia, Alex Marchioni, Fabio Pareschi, Riccardo Rovatti, Gianluca Setti
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10 May 2019 - 10:34am
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[1] Mauro Mangia, Alex Marchioni, Fabio Pareschi, Riccardo Rovatti, Gianluca Setti, "Chained Compressed Sensing for IoT Node Security", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4343. Accessed: Sep. 21, 2019.
@article{4343-19,
url = {http://sigport.org/4343},
author = {Mauro Mangia; Alex Marchioni; Fabio Pareschi; Riccardo Rovatti; Gianluca Setti },
publisher = {IEEE SigPort},
title = {Chained Compressed Sensing for IoT Node Security},
year = {2019} }
TY - EJOUR
T1 - Chained Compressed Sensing for IoT Node Security
AU - Mauro Mangia; Alex Marchioni; Fabio Pareschi; Riccardo Rovatti; Gianluca Setti
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4343
ER -
Mauro Mangia, Alex Marchioni, Fabio Pareschi, Riccardo Rovatti, Gianluca Setti. (2019). Chained Compressed Sensing for IoT Node Security. IEEE SigPort. http://sigport.org/4343
Mauro Mangia, Alex Marchioni, Fabio Pareschi, Riccardo Rovatti, Gianluca Setti, 2019. Chained Compressed Sensing for IoT Node Security. Available at: http://sigport.org/4343.
Mauro Mangia, Alex Marchioni, Fabio Pareschi, Riccardo Rovatti, Gianluca Setti. (2019). "Chained Compressed Sensing for IoT Node Security." Web.
1. Mauro Mangia, Alex Marchioni, Fabio Pareschi, Riccardo Rovatti, Gianluca Setti. Chained Compressed Sensing for IoT Node Security [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4343

ENCRYPTED SPEECH RECOGNITION USING DEEP POLYNOMIAL NETWORKS


The cloud-based speech recognition/API provides developers or enterprises an easy way to create speech-enabled features in their applications. However, sending audios about personal or company internal information to the cloud, raises concerns about the privacy and security issues. The recognition results generated in cloud may also reveal some sensitive information. This paper proposes a deep polynomial network (DPN) that can be applied to the encrypted speech as an acoustic model. It allows clients to send their data in an encrypted form to the cloud to ensure that their data remains confidential, at mean while the DPN can still make frame-level predictions over the encrypted speech and return them in encrypted form. One good property of the DPN is that it can be trained on unencrypted speech features in the traditional way. To keep the cloud away from the raw audio and recognition results, a cloud-local joint decoding framework is also proposed. We demonstrate the effectiveness of model and framework on the Switchboard and Cortana voice assistant tasks with small performance degradation and latency increased comparing with the traditional cloud-based DNNs.
https://ieeexplore.ieee.org/document/8683721

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Authors:
Yifan Gong, Dong YU
Submitted On:
8 May 2019 - 4:21am
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[1] Yifan Gong, Dong YU, " ENCRYPTED SPEECH RECOGNITION USING DEEP POLYNOMIAL NETWORKS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4045. Accessed: Sep. 21, 2019.
@article{4045-19,
url = {http://sigport.org/4045},
author = {Yifan Gong; Dong YU },
publisher = {IEEE SigPort},
title = { ENCRYPTED SPEECH RECOGNITION USING DEEP POLYNOMIAL NETWORKS},
year = {2019} }
TY - EJOUR
T1 - ENCRYPTED SPEECH RECOGNITION USING DEEP POLYNOMIAL NETWORKS
AU - Yifan Gong; Dong YU
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4045
ER -
Yifan Gong, Dong YU. (2019). ENCRYPTED SPEECH RECOGNITION USING DEEP POLYNOMIAL NETWORKS. IEEE SigPort. http://sigport.org/4045
Yifan Gong, Dong YU, 2019. ENCRYPTED SPEECH RECOGNITION USING DEEP POLYNOMIAL NETWORKS. Available at: http://sigport.org/4045.
Yifan Gong, Dong YU. (2019). " ENCRYPTED SPEECH RECOGNITION USING DEEP POLYNOMIAL NETWORKS." Web.
1. Yifan Gong, Dong YU. ENCRYPTED SPEECH RECOGNITION USING DEEP POLYNOMIAL NETWORKS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4045

A FORMAT-COMPLIANT SELECTIVE SECRET 3D OBJECT SHARING SCHEME BASED ON SHAMIR’S SCHEME


New issues have arisen in the creation of 3D objects linked to collaboration in 3D workflows. In this kind of usage it may be necessary to allow access and to share a lower quality file of a 3D object for some collaborators.
In this paper, we present a Format-Compliant Selective Secret 3D Object Sharing (FCSS3DOS) scheme, which visually protects the content using geometrical distortions by selectively sharing bits of a 3D object's geometry using Shamir's Secret Sharing scheme.

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Authors:
Jean-Pierre Pedeboy
Submitted On:
8 May 2019 - 3:53am
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[1] Jean-Pierre Pedeboy, "A FORMAT-COMPLIANT SELECTIVE SECRET 3D OBJECT SHARING SCHEME BASED ON SHAMIR’S SCHEME", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4039. Accessed: Sep. 21, 2019.
@article{4039-19,
url = {http://sigport.org/4039},
author = {Jean-Pierre Pedeboy },
publisher = {IEEE SigPort},
title = {A FORMAT-COMPLIANT SELECTIVE SECRET 3D OBJECT SHARING SCHEME BASED ON SHAMIR’S SCHEME},
year = {2019} }
TY - EJOUR
T1 - A FORMAT-COMPLIANT SELECTIVE SECRET 3D OBJECT SHARING SCHEME BASED ON SHAMIR’S SCHEME
AU - Jean-Pierre Pedeboy
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4039
ER -
Jean-Pierre Pedeboy. (2019). A FORMAT-COMPLIANT SELECTIVE SECRET 3D OBJECT SHARING SCHEME BASED ON SHAMIR’S SCHEME. IEEE SigPort. http://sigport.org/4039
Jean-Pierre Pedeboy, 2019. A FORMAT-COMPLIANT SELECTIVE SECRET 3D OBJECT SHARING SCHEME BASED ON SHAMIR’S SCHEME. Available at: http://sigport.org/4039.
Jean-Pierre Pedeboy. (2019). "A FORMAT-COMPLIANT SELECTIVE SECRET 3D OBJECT SHARING SCHEME BASED ON SHAMIR’S SCHEME." Web.
1. Jean-Pierre Pedeboy. A FORMAT-COMPLIANT SELECTIVE SECRET 3D OBJECT SHARING SCHEME BASED ON SHAMIR’S SCHEME [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4039

Learning Sensitive Images using Generative Models


The sheer amount of personal data being transmitted to cloud services and the ubiquity of cellphones cameras and various sensors have provoked a privacy concern among many people. On the other hand, the recent phenomenal growth of deep learning that brings advancements in almost every aspect of human life is heavily dependent on the access to data, including sensitive images, medical records, etc. Therefore, there is a need for a mechanism that transforms sensitive data in such a way as to preserves the privacy of individuals, yet still be useful for deep learning algorithms.

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Authors:
Sen-ching Samson Cheung, Herb Wildfeuer, Mehdi Nikkhah, Xiaoqing Zhu, Wai-tian Tan
Submitted On:
4 October 2018 - 5:40pm
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Learning Sensitive Images using Generative Models

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[1] Sen-ching Samson Cheung, Herb Wildfeuer, Mehdi Nikkhah, Xiaoqing Zhu, Wai-tian Tan, "Learning Sensitive Images using Generative Models", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3467. Accessed: Sep. 21, 2019.
@article{3467-18,
url = {http://sigport.org/3467},
author = {Sen-ching Samson Cheung; Herb Wildfeuer; Mehdi Nikkhah; Xiaoqing Zhu; Wai-tian Tan },
publisher = {IEEE SigPort},
title = {Learning Sensitive Images using Generative Models},
year = {2018} }
TY - EJOUR
T1 - Learning Sensitive Images using Generative Models
AU - Sen-ching Samson Cheung; Herb Wildfeuer; Mehdi Nikkhah; Xiaoqing Zhu; Wai-tian Tan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3467
ER -
Sen-ching Samson Cheung, Herb Wildfeuer, Mehdi Nikkhah, Xiaoqing Zhu, Wai-tian Tan. (2018). Learning Sensitive Images using Generative Models. IEEE SigPort. http://sigport.org/3467
Sen-ching Samson Cheung, Herb Wildfeuer, Mehdi Nikkhah, Xiaoqing Zhu, Wai-tian Tan, 2018. Learning Sensitive Images using Generative Models. Available at: http://sigport.org/3467.
Sen-ching Samson Cheung, Herb Wildfeuer, Mehdi Nikkhah, Xiaoqing Zhu, Wai-tian Tan. (2018). "Learning Sensitive Images using Generative Models." Web.
1. Sen-ching Samson Cheung, Herb Wildfeuer, Mehdi Nikkhah, Xiaoqing Zhu, Wai-tian Tan. Learning Sensitive Images using Generative Models [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3467

On Covert Communication Over Infinite-Bandwidth Gaussian Channels

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24 June 2018 - 3:14pm
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[1] , "On Covert Communication Over Infinite-Bandwidth Gaussian Channels", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3327. Accessed: Sep. 21, 2019.
@article{3327-18,
url = {http://sigport.org/3327},
author = { },
publisher = {IEEE SigPort},
title = {On Covert Communication Over Infinite-Bandwidth Gaussian Channels},
year = {2018} }
TY - EJOUR
T1 - On Covert Communication Over Infinite-Bandwidth Gaussian Channels
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3327
ER -
. (2018). On Covert Communication Over Infinite-Bandwidth Gaussian Channels. IEEE SigPort. http://sigport.org/3327
, 2018. On Covert Communication Over Infinite-Bandwidth Gaussian Channels. Available at: http://sigport.org/3327.
. (2018). "On Covert Communication Over Infinite-Bandwidth Gaussian Channels." Web.
1. . On Covert Communication Over Infinite-Bandwidth Gaussian Channels [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3327

Mutual-Information-Private Online Gradient Descent Algorithm


A user implemented privacy preservation mechanism is proposed for the online gradient descent (OGD) algorithm. Privacy is measured through the information leakage as quantified by the mutual information between the usersʼ outputs and learnerʼs inputs. The input perturbation mechanism proposed can be implemented by individual users with a space and time complexity that is independent of the horizon T. For the proposed mechanism, the information leakage is shown to be bounded by the Gaussian channel capacity in the full information setting.

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Authors:
Ruochi Zhang, Parv Venkitasubramaniam
Submitted On:
23 April 2018 - 1:52am
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[1] Ruochi Zhang, Parv Venkitasubramaniam, "Mutual-Information-Private Online Gradient Descent Algorithm", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3145. Accessed: Sep. 21, 2019.
@article{3145-18,
url = {http://sigport.org/3145},
author = {Ruochi Zhang; Parv Venkitasubramaniam },
publisher = {IEEE SigPort},
title = {Mutual-Information-Private Online Gradient Descent Algorithm},
year = {2018} }
TY - EJOUR
T1 - Mutual-Information-Private Online Gradient Descent Algorithm
AU - Ruochi Zhang; Parv Venkitasubramaniam
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3145
ER -
Ruochi Zhang, Parv Venkitasubramaniam. (2018). Mutual-Information-Private Online Gradient Descent Algorithm. IEEE SigPort. http://sigport.org/3145
Ruochi Zhang, Parv Venkitasubramaniam, 2018. Mutual-Information-Private Online Gradient Descent Algorithm. Available at: http://sigport.org/3145.
Ruochi Zhang, Parv Venkitasubramaniam. (2018). "Mutual-Information-Private Online Gradient Descent Algorithm." Web.
1. Ruochi Zhang, Parv Venkitasubramaniam. Mutual-Information-Private Online Gradient Descent Algorithm [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3145

Privacy-Preserving Outsourced Media Search Using Secure Sparse Ternary Codes


In this paper, we propose a privacy-preserving framework for outsourced media search applications. Considering three parties, a data owner, clients and a server, the data owner outsources the description of his data to an external server, which provides a search service to clients on the behalf of the data owner. The proposed framework is based on a sparsifying transform with ambiguization, which consists of a trained linear map, an element-wise nonlinearity and a privacy amplification.

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Authors:
Behrooz Razeghi, Slava Voloshynovskiy
Submitted On:
14 April 2018 - 3:01pm
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Privacy-Preserving Outsourced Media Search

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[1] Behrooz Razeghi, Slava Voloshynovskiy, "Privacy-Preserving Outsourced Media Search Using Secure Sparse Ternary Codes", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2847. Accessed: Sep. 21, 2019.
@article{2847-18,
url = {http://sigport.org/2847},
author = {Behrooz Razeghi; Slava Voloshynovskiy },
publisher = {IEEE SigPort},
title = {Privacy-Preserving Outsourced Media Search Using Secure Sparse Ternary Codes},
year = {2018} }
TY - EJOUR
T1 - Privacy-Preserving Outsourced Media Search Using Secure Sparse Ternary Codes
AU - Behrooz Razeghi; Slava Voloshynovskiy
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2847
ER -
Behrooz Razeghi, Slava Voloshynovskiy. (2018). Privacy-Preserving Outsourced Media Search Using Secure Sparse Ternary Codes. IEEE SigPort. http://sigport.org/2847
Behrooz Razeghi, Slava Voloshynovskiy, 2018. Privacy-Preserving Outsourced Media Search Using Secure Sparse Ternary Codes. Available at: http://sigport.org/2847.
Behrooz Razeghi, Slava Voloshynovskiy. (2018). "Privacy-Preserving Outsourced Media Search Using Secure Sparse Ternary Codes." Web.
1. Behrooz Razeghi, Slava Voloshynovskiy. Privacy-Preserving Outsourced Media Search Using Secure Sparse Ternary Codes [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2847

On the Indistinguishability of Compressed Encryption With Partial Unitary Sensing Matrices

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16 November 2017 - 8:34am
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[1] , "On the Indistinguishability of Compressed Encryption With Partial Unitary Sensing Matrices", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2361. Accessed: Sep. 21, 2019.
@article{2361-17,
url = {http://sigport.org/2361},
author = { },
publisher = {IEEE SigPort},
title = {On the Indistinguishability of Compressed Encryption With Partial Unitary Sensing Matrices},
year = {2017} }
TY - EJOUR
T1 - On the Indistinguishability of Compressed Encryption With Partial Unitary Sensing Matrices
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2361
ER -
. (2017). On the Indistinguishability of Compressed Encryption With Partial Unitary Sensing Matrices. IEEE SigPort. http://sigport.org/2361
, 2017. On the Indistinguishability of Compressed Encryption With Partial Unitary Sensing Matrices. Available at: http://sigport.org/2361.
. (2017). "On the Indistinguishability of Compressed Encryption With Partial Unitary Sensing Matrices." Web.
1. . On the Indistinguishability of Compressed Encryption With Partial Unitary Sensing Matrices [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2361

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