Sorry, you need to enable JavaScript to visit this website.

Statistical Signal Processing

On design of optimal smart meter privacy control strategy against adversarial MAP detection


We study the optimal control problem of the maximum a posteriori (MAP) state sequence detection of an adversary using smart meter data. The privacy leakage is measured using the Bayesian risk and the privacy-enhancing control is achieved in real-time using an energy storage system. The control strategy is designed to minimize the expected performance of a non-causal adversary at each time instant. With a discrete-state Markov model, we study two detection problems: when the adversary is unaware or aware of the control.

Paper Details

Authors:
Tobias J. Oechtering
Submitted On:
14 May 2020 - 12:00pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ON DESIGN OF OPTIMAL SMART METER PRIVACY CONTROL STRATEGY AGAINST ADVERSARIAL MAP DETECTION

(17)

Subscribe

[1] Tobias J. Oechtering, "On design of optimal smart meter privacy control strategy against adversarial MAP detection", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5312. Accessed: Jul. 05, 2020.
@article{5312-20,
url = {http://sigport.org/5312},
author = { Tobias J. Oechtering },
publisher = {IEEE SigPort},
title = {On design of optimal smart meter privacy control strategy against adversarial MAP detection},
year = {2020} }
TY - EJOUR
T1 - On design of optimal smart meter privacy control strategy against adversarial MAP detection
AU - Tobias J. Oechtering
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5312
ER -
Tobias J. Oechtering. (2020). On design of optimal smart meter privacy control strategy against adversarial MAP detection. IEEE SigPort. http://sigport.org/5312
Tobias J. Oechtering, 2020. On design of optimal smart meter privacy control strategy against adversarial MAP detection. Available at: http://sigport.org/5312.
Tobias J. Oechtering. (2020). "On design of optimal smart meter privacy control strategy against adversarial MAP detection." Web.
1. Tobias J. Oechtering. On design of optimal smart meter privacy control strategy against adversarial MAP detection [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5312

ICASSP 2020


We address the problem of detection, in the frequency domain, of a M-dimensional time series modeled as the output of a M × K MIMO filter driven by a K-dimensional Gaussian white noise, and disturbed by an additive M-dimensional Gaussian col- ored noise. We consider the study of test statistics based of the Spectral Coherence Matrix (SCM) obtained as renormalization of the smoothed periodogram matrix of the observed time series over N samples, and with smoothing span B.

Paper Details

Authors:
Alexis Rosuel, Pascal Vallet, Philippe Loubaton, Xavier Mestre
Submitted On:
14 May 2020 - 8:44am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP_2020_pr_sentation.pdf

(13)

Subscribe

[1] Alexis Rosuel, Pascal Vallet, Philippe Loubaton, Xavier Mestre, "ICASSP 2020", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5294. Accessed: Jul. 05, 2020.
@article{5294-20,
url = {http://sigport.org/5294},
author = {Alexis Rosuel; Pascal Vallet; Philippe Loubaton; Xavier Mestre },
publisher = {IEEE SigPort},
title = {ICASSP 2020},
year = {2020} }
TY - EJOUR
T1 - ICASSP 2020
AU - Alexis Rosuel; Pascal Vallet; Philippe Loubaton; Xavier Mestre
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5294
ER -
Alexis Rosuel, Pascal Vallet, Philippe Loubaton, Xavier Mestre. (2020). ICASSP 2020. IEEE SigPort. http://sigport.org/5294
Alexis Rosuel, Pascal Vallet, Philippe Loubaton, Xavier Mestre, 2020. ICASSP 2020. Available at: http://sigport.org/5294.
Alexis Rosuel, Pascal Vallet, Philippe Loubaton, Xavier Mestre. (2020). "ICASSP 2020." Web.
1. Alexis Rosuel, Pascal Vallet, Philippe Loubaton, Xavier Mestre. ICASSP 2020 [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5294

BP-VB-EP Based Static and Dynamic Sparse Bayesian Learning

Paper Details

Authors:
Dirk Slock
Submitted On:
13 May 2020 - 4:41pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

ICASSP20_presentation.pdf

(15)

Subscribe

[1] Dirk Slock, "BP-VB-EP Based Static and Dynamic Sparse Bayesian Learning", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5133. Accessed: Jul. 05, 2020.
@article{5133-20,
url = {http://sigport.org/5133},
author = {Dirk Slock },
publisher = {IEEE SigPort},
title = {BP-VB-EP Based Static and Dynamic Sparse Bayesian Learning},
year = {2020} }
TY - EJOUR
T1 - BP-VB-EP Based Static and Dynamic Sparse Bayesian Learning
AU - Dirk Slock
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5133
ER -
Dirk Slock. (2020). BP-VB-EP Based Static and Dynamic Sparse Bayesian Learning. IEEE SigPort. http://sigport.org/5133
Dirk Slock, 2020. BP-VB-EP Based Static and Dynamic Sparse Bayesian Learning. Available at: http://sigport.org/5133.
Dirk Slock. (2020). "BP-VB-EP Based Static and Dynamic Sparse Bayesian Learning." Web.
1. Dirk Slock. BP-VB-EP Based Static and Dynamic Sparse Bayesian Learning [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5133

Entropy Coders Based on the Splitting of Lexicographic Intervals

Paper Details

Authors:
Submitted On:
24 April 2020 - 2:41pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Session:
Document Year:
Cite

Document Files

poster.pdf

(31)

Subscribe

[1] , "Entropy Coders Based on the Splitting of Lexicographic Intervals", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5103. Accessed: Jul. 05, 2020.
@article{5103-20,
url = {http://sigport.org/5103},
author = { },
publisher = {IEEE SigPort},
title = {Entropy Coders Based on the Splitting of Lexicographic Intervals},
year = {2020} }
TY - EJOUR
T1 - Entropy Coders Based on the Splitting of Lexicographic Intervals
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5103
ER -
. (2020). Entropy Coders Based on the Splitting of Lexicographic Intervals. IEEE SigPort. http://sigport.org/5103
, 2020. Entropy Coders Based on the Splitting of Lexicographic Intervals. Available at: http://sigport.org/5103.
. (2020). "Entropy Coders Based on the Splitting of Lexicographic Intervals." Web.
1. . Entropy Coders Based on the Splitting of Lexicographic Intervals [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5103

Estimating Structural Missing Values via Low-tubal-rank Tensor Completion


The recently proposed Tensor Nuclear Norm (TNN) minimization has been widely used for tensor completion. However, previous works didn’t consider the structural difference between the observed data and missing data, which widely exists in many applications. In this paper, we propose to incorporate a constraint item on the missing values into low-tubal-rank tensor completion to promote the structural hypothesis

Paper Details

Authors:
Hailin Wang, Feng Zhang, Jianjun Wang, Yao Wang
Submitted On:
16 April 2020 - 3:15am
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

Estimating Structural Missing Values via Low-tubal-rank Tensor Completion.pdf

(37)

Keywords

Additional Categories

Subscribe

[1] Hailin Wang, Feng Zhang, Jianjun Wang, Yao Wang, "Estimating Structural Missing Values via Low-tubal-rank Tensor Completion", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5098. Accessed: Jul. 05, 2020.
@article{5098-20,
url = {http://sigport.org/5098},
author = {Hailin Wang; Feng Zhang; Jianjun Wang; Yao Wang },
publisher = {IEEE SigPort},
title = {Estimating Structural Missing Values via Low-tubal-rank Tensor Completion},
year = {2020} }
TY - EJOUR
T1 - Estimating Structural Missing Values via Low-tubal-rank Tensor Completion
AU - Hailin Wang; Feng Zhang; Jianjun Wang; Yao Wang
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5098
ER -
Hailin Wang, Feng Zhang, Jianjun Wang, Yao Wang. (2020). Estimating Structural Missing Values via Low-tubal-rank Tensor Completion. IEEE SigPort. http://sigport.org/5098
Hailin Wang, Feng Zhang, Jianjun Wang, Yao Wang, 2020. Estimating Structural Missing Values via Low-tubal-rank Tensor Completion. Available at: http://sigport.org/5098.
Hailin Wang, Feng Zhang, Jianjun Wang, Yao Wang. (2020). "Estimating Structural Missing Values via Low-tubal-rank Tensor Completion." Web.
1. Hailin Wang, Feng Zhang, Jianjun Wang, Yao Wang. Estimating Structural Missing Values via Low-tubal-rank Tensor Completion [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5098

Particle Filtering on the Complex Stiefel Manifold with Application to Subspace Tracking


In this paper, we extend previous particle filtering methods whose states were constrained to the (real) Stiefel manifold to the complex case. The method is then applied to a Bayesian formulation of the subspace tracking problem. To implement the proposed particle filter, we modify a previous MCMC algorithm so as to simulate from densities defined on the complex manifold. Also, to compute subspace estimates from particle approximations, we extend existing averaging methods to complex Grassmannians.

Paper Details

Authors:
Marcelo G. S. Bruno
Submitted On:
13 April 2020 - 9:10pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Slides

(32)

Subscribe

[1] Marcelo G. S. Bruno, "Particle Filtering on the Complex Stiefel Manifold with Application to Subspace Tracking", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5092. Accessed: Jul. 05, 2020.
@article{5092-20,
url = {http://sigport.org/5092},
author = {Marcelo G. S. Bruno },
publisher = {IEEE SigPort},
title = {Particle Filtering on the Complex Stiefel Manifold with Application to Subspace Tracking},
year = {2020} }
TY - EJOUR
T1 - Particle Filtering on the Complex Stiefel Manifold with Application to Subspace Tracking
AU - Marcelo G. S. Bruno
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5092
ER -
Marcelo G. S. Bruno. (2020). Particle Filtering on the Complex Stiefel Manifold with Application to Subspace Tracking. IEEE SigPort. http://sigport.org/5092
Marcelo G. S. Bruno, 2020. Particle Filtering on the Complex Stiefel Manifold with Application to Subspace Tracking. Available at: http://sigport.org/5092.
Marcelo G. S. Bruno. (2020). "Particle Filtering on the Complex Stiefel Manifold with Application to Subspace Tracking." Web.
1. Marcelo G. S. Bruno. Particle Filtering on the Complex Stiefel Manifold with Application to Subspace Tracking [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5092

On the Robustness of Causal Discovery with Additive Noise Models on Discrete Data

Paper Details

Authors:
Kang Du, Austin Goddard, Yu Xiang
Submitted On:
31 March 2020 - 12:58am
Short Link:
Type:
Event:
Session:
Document Year:
Cite

Document Files

On the Robustness of Causal Discovery with Additive Noise Models on Discrete Data.pdf

(28)

Subscribe

[1] Kang Du, Austin Goddard, Yu Xiang, "On the Robustness of Causal Discovery with Additive Noise Models on Discrete Data", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5071. Accessed: Jul. 05, 2020.
@article{5071-20,
url = {http://sigport.org/5071},
author = {Kang Du; Austin Goddard; Yu Xiang },
publisher = {IEEE SigPort},
title = {On the Robustness of Causal Discovery with Additive Noise Models on Discrete Data},
year = {2020} }
TY - EJOUR
T1 - On the Robustness of Causal Discovery with Additive Noise Models on Discrete Data
AU - Kang Du; Austin Goddard; Yu Xiang
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5071
ER -
Kang Du, Austin Goddard, Yu Xiang. (2020). On the Robustness of Causal Discovery with Additive Noise Models on Discrete Data. IEEE SigPort. http://sigport.org/5071
Kang Du, Austin Goddard, Yu Xiang, 2020. On the Robustness of Causal Discovery with Additive Noise Models on Discrete Data. Available at: http://sigport.org/5071.
Kang Du, Austin Goddard, Yu Xiang. (2020). "On the Robustness of Causal Discovery with Additive Noise Models on Discrete Data." Web.
1. Kang Du, Austin Goddard, Yu Xiang. On the Robustness of Causal Discovery with Additive Noise Models on Discrete Data [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5071

A whiteness test based on the spectral measure of large non-Hermitian random matrices


In the context of multivariate time series, a whiteness test against an MA(1)
correlation model is proposed. This test is built on the eigenvalue
distribution (spectral measure) of the non-Hermitian one-lag sample
autocovariance matrix, instead of its singular value distribution. The large
dimensional limit spectral measure of this matrix is derived. To obtain this
result, a control over the smallest singular value of a related random matrix
is provided. Numerical simulations show the excellent performance of this
test.

Paper Details

Authors:
Arup Bose, Walid Hachem
Submitted On:
10 February 2020 - 4:17am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Bose/Hachem ICASSP paper

(33)

Subscribe

[1] Arup Bose, Walid Hachem, "A whiteness test based on the spectral measure of large non-Hermitian random matrices", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/4973. Accessed: Jul. 05, 2020.
@article{4973-20,
url = {http://sigport.org/4973},
author = {Arup Bose; Walid Hachem },
publisher = {IEEE SigPort},
title = {A whiteness test based on the spectral measure of large non-Hermitian random matrices},
year = {2020} }
TY - EJOUR
T1 - A whiteness test based on the spectral measure of large non-Hermitian random matrices
AU - Arup Bose; Walid Hachem
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/4973
ER -
Arup Bose, Walid Hachem. (2020). A whiteness test based on the spectral measure of large non-Hermitian random matrices. IEEE SigPort. http://sigport.org/4973
Arup Bose, Walid Hachem, 2020. A whiteness test based on the spectral measure of large non-Hermitian random matrices. Available at: http://sigport.org/4973.
Arup Bose, Walid Hachem. (2020). "A whiteness test based on the spectral measure of large non-Hermitian random matrices." Web.
1. Arup Bose, Walid Hachem. A whiteness test based on the spectral measure of large non-Hermitian random matrices [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/4973

ROBUST M-ESTIMATION BASED MATRIX COMPLETION


Conventional approaches to matrix completion are sensitive to outliers and impulsive noise. This paper develops robust and computationally efficient M-estimation based matrix completion algorithms. By appropriately arranging the observed entries, and then applying alternating minimization, the robust matrix completion problem is converted into a set of regression M-estimation problems. Making use of differ- entiable loss functions, the proposed algorithm overcomes a weakness of the lp-loss (p ≤ 1), which easily gets stuck in an inferior point.

Paper Details

Authors:
Michael Muma, Wen-Jun Zeng, Abdelhak M. Zoubir
Submitted On:
27 May 2019 - 11:28am
Short Link:
Type:
Event:
Paper Code:
Document Year:
Cite

Document Files

ICASSP_2019_Robust_M_Estimation_Based_Matrix_Completion_Poster.pdf

(137)

Subscribe

[1] Michael Muma, Wen-Jun Zeng, Abdelhak M. Zoubir, "ROBUST M-ESTIMATION BASED MATRIX COMPLETION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4561. Accessed: Jul. 05, 2020.
@article{4561-19,
url = {http://sigport.org/4561},
author = {Michael Muma; Wen-Jun Zeng; Abdelhak M. Zoubir },
publisher = {IEEE SigPort},
title = {ROBUST M-ESTIMATION BASED MATRIX COMPLETION},
year = {2019} }
TY - EJOUR
T1 - ROBUST M-ESTIMATION BASED MATRIX COMPLETION
AU - Michael Muma; Wen-Jun Zeng; Abdelhak M. Zoubir
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4561
ER -
Michael Muma, Wen-Jun Zeng, Abdelhak M. Zoubir. (2019). ROBUST M-ESTIMATION BASED MATRIX COMPLETION. IEEE SigPort. http://sigport.org/4561
Michael Muma, Wen-Jun Zeng, Abdelhak M. Zoubir, 2019. ROBUST M-ESTIMATION BASED MATRIX COMPLETION. Available at: http://sigport.org/4561.
Michael Muma, Wen-Jun Zeng, Abdelhak M. Zoubir. (2019). "ROBUST M-ESTIMATION BASED MATRIX COMPLETION." Web.
1. Michael Muma, Wen-Jun Zeng, Abdelhak M. Zoubir. ROBUST M-ESTIMATION BASED MATRIX COMPLETION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4561

Improving Graph Trend Filtering with Non-Convex Penalties


In this paper, we study the denoising of piecewise smooth graph sig-nals that exhibit inhomogeneous levels of smoothness over a graph. We extend the graph trend filtering framework to a family of non-convex regularizers that exhibit superior recovery performance overexisting convex ones. We present theoretical results in the form ofasymptotic error rates for both generic and specialized graph models. We further present an ADMM-based algorithm to solve the proposedoptimization problem and analyze its convergence.

Paper Details

Authors:
Rohan Varma, Jelena Kovačević
Submitted On:
9 June 2019 - 8:24pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP_poster.pdf

(171)

Keywords

Additional Categories

Subscribe

[1] Rohan Varma, Jelena Kovačević, "Improving Graph Trend Filtering with Non-Convex Penalties", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4551. Accessed: Jul. 05, 2020.
@article{4551-19,
url = {http://sigport.org/4551},
author = {Rohan Varma; Jelena Kovačević },
publisher = {IEEE SigPort},
title = {Improving Graph Trend Filtering with Non-Convex Penalties},
year = {2019} }
TY - EJOUR
T1 - Improving Graph Trend Filtering with Non-Convex Penalties
AU - Rohan Varma; Jelena Kovačević
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4551
ER -
Rohan Varma, Jelena Kovačević. (2019). Improving Graph Trend Filtering with Non-Convex Penalties. IEEE SigPort. http://sigport.org/4551
Rohan Varma, Jelena Kovačević, 2019. Improving Graph Trend Filtering with Non-Convex Penalties. Available at: http://sigport.org/4551.
Rohan Varma, Jelena Kovačević. (2019). "Improving Graph Trend Filtering with Non-Convex Penalties." Web.
1. Rohan Varma, Jelena Kovačević. Improving Graph Trend Filtering with Non-Convex Penalties [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4551

Pages