TY - JOUR
T1 - Comparative Analysis of Load-Shaping-Based Privacy Preservation Strategies in a Smart Grid
AU - Kement, Cihan Emre
AU - Gultekin, Hakan
AU - Tavli, Bulent
AU - Girici, Tolga
AU - Uludag, Suleyman
N1 - Publisher Copyright:
© 2005-2012 IEEE.
PY - 2017/12
Y1 - 2017/12
N2 - A key enabler for the smart grid is the fine-grained monitoring of power utilization. Although such a mechanism is helpful in the optimization of the whole electricity generation, distribution, and consumption cycle, it also creates opportunities for the potential adversaries in deducing the activities and habits of the subscribers. In fact, by utilizing the standard and readily available tools of nonintrusive load monitoring (NILM) techniques on the metered electricity data, many details of customers' personal lives can be easily discovered. Therefore, prevention of such adversarial exploitations is of utmost importance for privacy protection. One strong privacy preservation approach is the modification of the metered data through the use of on-site storage units in conjunction with renewable energy resources. In this study, we introduce a novel mathematical programming framework to model eight privacy-enhanced power-scheduling strategies inspired and elicited from the literature. We employ all the relevant techniques for the modification of the actual electricity utilization (i.e., on-site battery, renewable energy resources, and appliance load moderation). Our evaluation framework is the first in the literature, to the best of our knowledge, for a comprehensive and fair comparison of the load-shaping techniques for privacy preservation. In addition to the privacy concerns, we consider monetary cost and disutility of the users in our objective functions. Evaluation results show that privacy preservation strategies in the literature differ significantly in terms of privacy, cost, and disutility metrics.
AB - A key enabler for the smart grid is the fine-grained monitoring of power utilization. Although such a mechanism is helpful in the optimization of the whole electricity generation, distribution, and consumption cycle, it also creates opportunities for the potential adversaries in deducing the activities and habits of the subscribers. In fact, by utilizing the standard and readily available tools of nonintrusive load monitoring (NILM) techniques on the metered electricity data, many details of customers' personal lives can be easily discovered. Therefore, prevention of such adversarial exploitations is of utmost importance for privacy protection. One strong privacy preservation approach is the modification of the metered data through the use of on-site storage units in conjunction with renewable energy resources. In this study, we introduce a novel mathematical programming framework to model eight privacy-enhanced power-scheduling strategies inspired and elicited from the literature. We employ all the relevant techniques for the modification of the actual electricity utilization (i.e., on-site battery, renewable energy resources, and appliance load moderation). Our evaluation framework is the first in the literature, to the best of our knowledge, for a comprehensive and fair comparison of the load-shaping techniques for privacy preservation. In addition to the privacy concerns, we consider monetary cost and disutility of the users in our objective functions. Evaluation results show that privacy preservation strategies in the literature differ significantly in terms of privacy, cost, and disutility metrics.
KW - Goal programming
KW - load shaping
KW - mixed-integer programming (MIP)
KW - mixed-integer quadratic programming (MIQP)
KW - multiobjective programming
KW - nonintrusive load monitoring (NILM)
KW - privacy
KW - renewable energy
KW - smart grid
UR - http://www.scopus.com/inward/record.url?scp=85023767582&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85023767582&partnerID=8YFLogxK
U2 - 10.1109/TII.2017.2718666
DO - 10.1109/TII.2017.2718666
M3 - Article
AN - SCOPUS:85023767582
SN - 1551-3203
VL - 13
SP - 3226
EP - 3235
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 6
M1 - 7956260
ER -