Evaluation of the Precision-Privacy Tradeoff of Data Perturbation for Smart Metering

Abstract: Smart grid users and standardization committees require that utilities and third parties collecting metering data employ techniques for limiting the level of precision of the gathered household measurements to a granularity no finer than what is required for providing the expected service. Data aggregation and data perturbation are two such techniques. This paper provides quantitative means to identify a tradeoff between the aggregation set size, the precision on the aggregated measurements, and the privacy level. This is achieved by formally defining an attack to the privacy of an individual user and calculating how much its success probability is reduced by applying data perturbation. Under the assumption of time-correlation of the measurements, colored noise can be used to even further reduce the success probability. The tightness of the analytical results is evaluated by comparing them to experimental data

A privacy-friendly game-theoretic distributed scheduling system for domestic appliances

open3Game-theoretic Demand Side Management (DSM) systems have been investigated as a decentralized approach for the collaborative scheduling of the usage of domestic electrical appliances within a set of households. Such systems allow for the shifting of the starting time of deferrable devices according to the current energy price or power grid condition, in order to reduce the individual monthly bill or to adjust the power load experienced by the grid while meeting the users’ preferences about the time of use. The drawback of DSM distributed protocols is that they require each user to communicate his/her own energy consumption patterns to the other users, which may leak sensitive information regarding private habits. This paper proposes a distributed Privacy-Friendly DSM system which preserves users’ privacy by integrating data aggregation and perturbation techniques: users decide their schedule according to aggregated consumption measurements perturbed by means of Additive White Gaussian Noise (AWGN). We evaluate the noise power and the size of the set of users required to achieve a given privacy level, quantified by means of the Kullback-Leibler divergence. The performance of our proposed DSM system are compared to the ones obtained by a benchmark system which does not support privacy preservation in terms of social cost, peak demand and convergence time. Results show that privacy can be preserved at the cost of increasing the peak demand and the number of game iterations, whereas social cost is only marginally incremented.C Rottondi; A Barbato; G VerticaleRottondi, CRISTINA EMMA MARGHERITA; Barbato, Antimo; Verticale, Giacom

How to Integrate Machine-Learning Probabilistic Output in Integer Linear Programming: a case for RSA

We integrate machine-learning-based QoT estimation in reach constraints of an integer linear program (ILP) for routing and spectrum assignment (RSA), and develop an iterative solution for QoT-aware RSA. Results show above 30% spectrum savings compared to solving RSA with ILP using traditional margined reach computation

Dual-Stage Planning for Elastic Optical Networks Integrating Machine-Learning-Assisted QoT Estimation

Following the emergence of Elastic Optical Networks (EONs), Machine Learning (ML) has been intensively investigated as a promising methodology to address complex network management tasks, including, e.g., Quality of Transmission (QoT) estimation, fault management, and automatic adjustment of transmission parameters. Though several ML-based solutions for specific tasks have been proposed, how to integrate the outcome of such ML approaches inside Routing and Spectrum Assignment (RSA) models (which address the fundamental planning problem in EONs) is still an open research problem. In this study, we propose a dual-stage iterative RSA optimization framework that incorporates the QoT estimations provided by a ML regressor, used to define lightpaths' reach constraints, into a Mixed Integer Linear Programming (MILP) formulation. The first stage minimizes the overall spectrum occupation, whereas the second stage maximizes the minimum inter-channel spacing between neighbor channels, without increasing the overall spectrum occupation obtained in the previous stage. During the second stage, additional interference constraints are generated, and these constraints are then added to the MILP at the next iteration round to exclude those lightpaths combinations that would exhibit unacceptable QoT. Our illustrative numerical results on realistic EON instances show that the proposed ML-assisted framework achieves spectrum occupation savings up to 52.4% (around 33% on average) in comparison to a traditional MILP-based RSA framework that uses conservative reach constraints based on margined analytical models

Secure and Differentially Private Detection of Net Neutrality Violations by Means of Crowdsourced Measurements

Evaluating Network Neutrality requires comparing the quality of service experienced by multiple users served by different Internet Service Providers. Consequently, the issue of guaranteeing privacy-friendly network measurements has recently gained increasing interest. In this paper we propose a system which gathers throughput measurements from users of various applications and Internet services and stores it in a crowdsourced database, which can be queried by the users themselves to verify if their submitted measurements are compliant with the hypothesis of a neutral network. Since the crowdsourced data may disclose sensitive information about users and their habits, thus leading to potential privacy leakages, we adopt a privacy-preserving method based on randomized sampling and suppression of small clusters. Numerical results show that the proposed solution ensures a good trade-off between usefulness of the system, in terms of precision and recall of discriminated users, and privacy, in terms of differential privacy

To be neutral or not neutral? the in-network caching dilemma

Caching allows Internet Service Providers (ISPs) to reduce network traffic and Content Providers (CPs) to increase the offered QoS. However, when contents are encrypted, effective caching is possible only if ISPs and CPs cooperate. We suggest possible forms of non-discriminatory cooperation that make caching compliant with the principles of Net-Neutrality (NN

Transceivers and Spectrum Usage Minimization in Few-Mode Optical Networks

Metro-Area networks are likely to create the right conditions for the deployment of few-mode transmission (FMT) due to limited metro distances and rapidly increasing metro traffic. To address the new network design problems arising with the adoption of FMT, integer linear programming (ILP) formulations have already been developed to optimally assign modulation formats, baud rates, and transmission modes to lightpaths, but these formulations lack scalability, especially when they incorporate accurate constraints to capture inter-modal coupling. In this paper, we propose a heuristic approach for the routing, modulation format, baud rate and spectrum allocation in FMT networks with arbitrary topology, accounting for inter-modal coupling and for distance-Adaptive reaches of few-mode (specifically, up to five modes) signals generated by either full multi-in multi-out (MIMO) or low-complexity MIMO transceivers and for two different switching scenarios (i.e., spatial full-joint and fractional-joint switching). In our illustrative numerical analysis, we first confirm the quasi-optimality of our heuristic by comparing it to the optimal ILP solutions, and then we use our heuristic to identify which switching scenario and FMT transceiver technology minimize spectrum occupation and transceiver costs, depending on the relative costs of transceiver equipment and dark fiber leasing

A Tutorial on Machine Learning for Failure Management in Optical Networks

Failure management plays a role of capital importance in optical networks to avoid service disruptions and to satisfy customers' service level agreements. Machine learning (ML) promises to revolutionize the (mostly manual and human-driven) approaches in which failure management in optical networks has been traditionally managed, by introducing automated methods for failure prediction, detection, localization, and identification. This tutorial provides a gentle introduction to some ML techniques that have been recently applied in the field of the optical-network failure management. It then introduces a taxonomy to classify failure-management tasks and discusses possible applications of ML for these failure management tasks. Finally, for a reader interested in more implementative details, we provide a step-by-step description of how to solve a representative example of a practical failure-management task

Machine learning regression for QoT estimation of unestablished lightpaths

Estimating the quality of transmission (QoT) of a candidate lightpath prior to its establishment is of pivotal importance for effective decision making in resource allocation for optical networks. Several recent studies investigated machine learning (ML) methods to accurately predict whether the configuration of a prospective lightpath satisfies a given threshold on a QoT metric such as the generalized signal-To-noise ratio (GSNR) or the bit error rate. Given a set of features, the GSNR for a given lightpath configuration may still exhibit variations, as it depends on several other factors not captured by the features considered. It follows that the GSNR associated with a lightpath configuration can be modeled as a random variable and thus be characterized by a probability distribution function. However, most of the existing approaches attempt to directly answer the question is a given lightpath configuration (e.g., with a given modulation format) feasible on a certain path? but do not consider the additional benefit that estimating the entire statistical distribution of the metric under observation can provide. Hence, in this paper, we investigate how to employ ML regression approaches to estimate the distribution of the received GSNR of unestablished lightpaths. In particular, we discuss and assess the performance of three regression approaches by leveraging synthetic data obtained by means of two different data generation tools. We evaluate the performance of the three proposed approaches on a realistic network topology in terms of root mean squared error and R2 score and compare them against a baseline approach that simply predicts the GSNR mean value. Moreover, we provide a cost analysis by attributing penalties to incorrect deployment decisions and emphasize the benefits of leveraging the proposed estimation approaches from the point of view of a network operator, which is allowed to make more informed decisions about lightpath deployment with respect to state-of-The-Art QoT classification techniques