scholarly journals Research Challenges in Designing Differentially Private Text Generation Mechanisms

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Oluwaseyi Feyisetan ◽  
Abhinav Aggarwal ◽  
Zekun Xu ◽  
Nathanael Teissier
Keyword(s):  
Differential Privacy ◽  
Recent Literature ◽  
Text Generation ◽  
Trade Off ◽  
Privacy Amplification ◽  
Single Solution ◽  
User Data ◽  
Generation Mechanisms ◽  
High Level ◽  
User Trust

Accurately learning from user data while ensuring quantifiable privacy guarantees provides an opportunity to build better ML models while maintaining user trust. Recent literature has demonstrated the applicability of a generalized form of Differential Privacy to provide guarantees over text queries. Such mechanisms add privacy preserving noise to vectorial representations of text in high dimension and return a text based projection of the noisy vectors. However, these mechanisms are sub-optimal in their trade-off between privacy and utility. In this proposal paper, we describe some challenges in balancing this trade-off. At a high level, we provide two proposals: (1) a framework called LAC which defers some of the noise to a privacy amplification step and (2), an additional suite of three different techniques for calibrating the noise based on the local region around a word. Our objective in this paper is not to evaluate a single solution but to further the conversation on these challenges and chart pathways for building better mechanisms.

scholarly journals RECENT PROGRESS OF DIFFERENTIALLY PRIVATE FEDERATED LEARNING WITH THE SHUFFLE MODEL

2021 ◽  
Vol 8 (11) ◽  
pp. 55-75
Author(s):  
Moushira Abdallah Mohamed Ahmed ◽  
Shuhui Wu ◽  
Laure Deveriane Dushime ◽  
Yuanhong Tao
Keyword(s):  
Recent Progress ◽  
Differential Privacy ◽  
High Accuracy ◽  
Local Model ◽  
Modified Technique ◽  
Trade Off ◽  
Privacy Amplification ◽  
Analytic Learning ◽  
Limited Accuracy

The emerging of shuffle model has attracted considerable attention of scientists owing to his unique properties in solving the privacy problems in federated learning, specifically the trade off problem between privacy and utility in central and local model. Where, the central model relies on a trusted server which collects users’ raw data and then perturbs it. While in the local model all users perturb their data locally then they send their perturbed data to server. Both models have pron and con. The server in central model enjoys with high accuracy but the users suffer from insufficient privacy in contrast, the local model which provides sufficient privacy at users’ side but the server suffers from limited accuracy. Shuffle model has advanced property of hide position of input messages by perturbing it with perturbation π. Therefore, the scientists considered on adding shuffle model between users and servers to make the server untrusted where the users communicate with the server through the shuffle and boosting the privacy by adding perturbation π for users’ messages without increasing the noise level. Consequently, the usage of modified technique differential privacy federated learning with shuffle model will explores the gap between privacy and accuracy in both models. So this new model attracted many researchers in recent work. In this review, we initiate the analytic learning of a shuffled model for distributed differentially private mechanisms. We focused on the role of shuffle model for solving the problem between privacy and accuracy by summarizing the recent researches about shuffle model and its practical results. Furthermore, we present two types of shuffle, single shuffle and m shuffles with the statistical analysis for each one in boosting the privacy amplification of users with the same level of accuracy by reasoning the practical results of recent papers.

The Trade-off Between Privacy and Utility in Local Differential Privacy

2021 ◽  
Author(s):  
Mengqian Li ◽  
Youliang Tian ◽  
Junpeng Zhang ◽  
Dandan Fan ◽  
Dongmei Zhao
Keyword(s):  
Differential Privacy ◽  
Trade Off

scholarly journals An Exploratory Study into Trade-off Effects of Complexity, Accuracy, and Fluency on Young Learners’ Oral Task Repetition

2015 ◽  
Vol 31 ◽  
pp. 23 ◽  
Author(s):  
Evelyn Sample ◽  
Marije Michel
Keyword(s):  
Foreign Language ◽  
Language Learners ◽  
Recent Literature ◽  
Three Dimensions ◽  
Task Repetition ◽  
Trade Off ◽  
Efl Learners ◽  
Foreign Language Learners ◽  
De Se ◽  
The Difference

Studying task repetition for adult and young foreign language learners of English (EFL) has received growing interest in recent literature within the task-based approach (Bygate, 2009; Hawkes, 2012; Mackey, Kanganas, & Oliver, 2007; Pinter, 2007b). Earlier work suggests that second language (L2) learners benefit from repeating the same or a slightly different task. Task repetition has been shown to enhance fluency and may also add to complexity or accuracy of production. However, few investigations have taken a closer look at the underlying relationships between the three dimensions of task performance: complexity, accuracy, and fluency (CAF). Using Skehan’s (2009) trade-off hypothesis as an explanatory framework, our study aims to fill this gap by investigating interactions among CAF measures. We report on the repeated performances on an oral spot- the-difference task by six 9-year-old EFL learners. Mirroring earlier work, our data reveal significant increases of fluency through task repetition. Correlational analyses show that initial performances that benefit in one dimension come at the expense of another; by the third performance, however, trade-off effects disappear. Further qualitative explanations support our interpretation that with growing task-familiarity students are able to focus their attention on all three CAF dimensions simultaneously.Au sein de la littérature relative à l’approche fondée sur les tâches, on évoque de plus en plus d’études portant sur la répétition des tâches pour l’enseignement de l’anglais langue étrangère aux jeunes et aux adultes (Bygate, 2009; Hawkes, 2012; Mackey, Kanganas, & Oliver, 2007; Pinter, 2007b). Des études antérieures semblent indiquer que les apprenants en L2 profitent de la répétition de la même tâche ou d’une tâche légèrement différente. Il a été démontré que la répétition des tâches améliore la fluidité et qu’elle pourrait augmenter la complexité ou la précision de la production. Toutefois, peu d’études se sont penchées davantage sur les relations sous-jacentes entre les trois dimensions de l’exécution des tâches : la complexité, la précision et la fluidité. S’appuyant sur l’hypothèse du compromis de Skehan (2009) comme cadre explicatif, notre étude vise à combler cette lacune en examinant les interactions entre les mesures de ces trois éléments. Nous faisons rapport du rendement de six jeunes âgés de 9 ans qui apprennent l’anglais comme langue étrangère alors qu’ils répètent une tâche impliquant l’identification de différences. Nos données reproduisent les résultats de travaux antérieurs en ce qu’elles révèlent une amélioration significative de la fluidité par la répétition de tâches. Des analyses corrélationnelles indiquent que l’amélioration d’une dimension lors des exécutions initiales se fait aux dépens d’une autre; cet effet de compromis disparait, toutefois, à la troisième exécution. Des explications quali- tatives supplémentaires viennent appuyer notre interprétation selon laquelle la familiarité croissante que ressentent les élèves avec une tâche leur permet de se concentrer sur les trois dimensions (complexité, précision et fluidité) à la fois.

scholarly journals Manipulation Attacks in Local Differential Privacy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Albert Cheu ◽  
Adam Smith ◽  
Jonathan Ullman
Keyword(s):  
Systematic Study ◽  
Differential Privacy ◽  
Domain Size ◽  
User Data ◽  
Large Systems ◽  
Cryptographic Techniques ◽  
Fundamental Limitation ◽  
Theoretical Results ◽  
Different Levels ◽  
Privacy Level

Local differential privacy is a widely studied restriction on distributed algorithms that collect aggregates about sensitive user data, and is now deployed in several large systems. We initiate a systematic study of a fundamental limitation of locally differentially private protocols: they are highly vulnerable to adversarial manipulation. While any algorithm can be manipulated by adversaries who lie about their inputs, we show that any noninteractive locally differentially private protocol can be manipulated to a much greater extent---when the privacy level is high, or the domain size is large, a small fraction of users in the protocol can completely obscure the distribution of the honest users' input. We also construct protocols that are optimally robust to manipulation for a variety of common tasks in local differential privacy. Finally, we give simple experiments validating our  theoretical results, and demonstrating that protocols that are optimal without manipulation can have dramatically different levels of robustness to manipulation. Our results suggest caution when deploying local differential privacy and reinforce the importance of efficient cryptographic  techniques for the distributed emulation of centrally differentially private mechanisms.

scholarly journals Formaldehyde-Responsive Proteins TtmR and EfgA Reveal a Trade-off between Formaldehyde Resistance and Efficient Transition to Methylotrophy in Methylorubrum extorquens

2021 ◽  
Vol 203 (9) ◽  
Author(s):  
Jannell V. Bazurto ◽  
Eric L. Bruger ◽  
Jessica A. Lee ◽  
Leah B. Lambert ◽  
Christopher J. Marx
Keyword(s):  
Transcription Factors ◽  
Sole Source ◽  
Growth Delay ◽  
Enzymatic Reactions ◽  
Environmental Niche ◽  
Carbon Utilization ◽  
Trade Off ◽  
Content Type ◽  
Formaldehyde Sensing ◽  
High Level

ABSTRACT For bacteria to thrive, they must be well adapted to their environmental niche, which may involve specialized metabolism, timely adaptation to shifting environments, and/or the ability to mitigate numerous stressors. These attributes are highly dependent on cellular machinery that can sense both the external and intracellular environment. Methylorubrum extorquens is an extensively studied facultative methylotroph, an organism that can use single-carbon compounds as its sole source of carbon and energy. In methylotrophic metabolism, carbon flows through formaldehyde as a central metabolite; thus, formaldehyde is both an obligate metabolite and a metabolic stressor. Via the one-carbon dissimilation pathway, free formaldehyde is rapidly incorporated by formaldehyde activating enzyme (Fae), which is constitutively expressed at high levels. In the presence of elevated formaldehyde levels, a recently identified formaldehyde-sensing protein, EfgA, induces growth arrest. Here, we describe TtmR, a formaldehyde-responsive transcription factor that, like EfgA, modulates formaldehyde resistance. TtmR is a member of the MarR family of transcription factors and impacts the expression of 75 genes distributed throughout the genome, of which many encode transcription factors and/or are involved in stress response, including efgA. Notably, when M. extorquens is adapting its metabolic network during the transition to methylotrophy, efgA and ttmR mutants experience an imbalance in formaldehyde production and a notable growth delay. Although methylotrophy necessitates that M. extorquens maintains a relatively high level of formaldehyde tolerance, this work reveals a trade-off between formaldehyde resistance and the efficient transition to methylotrophic growth and suggests that TtmR and EfgA play a pivotal role in maintaining this balance. IMPORTANCE All organisms produce formaldehyde as a by-product of enzymatic reactions and as a degradation product of metabolites. The ubiquity of formaldehyde in cellular biology suggests that all organisms have evolved mechanisms of mitigating formaldehyde toxicity. However, formaldehyde sensing is poorly described, and the prevention of formaldehyde-induced damage is understood primarily in the context of detoxification. Here, we used an organism that is regularly exposed to elevated intracellular formaldehyde concentrations through high-flux one-carbon utilization pathways to gain insight into the role of formaldehyde-responsive proteins that modulate formaldehyde resistance. Using a combination of genetic and transcriptomic analyses, we identified dozens of genes putatively involved in formaldehyde resistance, determined the relationship between two different formaldehyde response systems, and identified an inherent trade-off between formaldehyde resistance and optimal transition to methylotrophic metabolism.

Privacy in Control and Dynamical Systems

2018 ◽  
Vol 1 (1) ◽  
pp. 309-332 ◽  
Author(s):  
Shuo Han ◽  
George J. Pappas
Keyword(s):  
Dynamical Systems ◽  
Smart Grids ◽  
Differential Privacy ◽  
Side Information ◽  
Sensitive Information ◽  
Efficient Operation ◽  
The Public ◽  
Rigorous Approach ◽  
Trade Offs ◽  
User Data

Many modern dynamical systems, such as smart grids and traffic networks, rely on user data for efficient operation. These data often contain sensitive information that the participating users do not wish to reveal to the public. One major challenge is to protect the privacy of participating users when utilizing user data. Over the past decade, differential privacy has emerged as a mathematically rigorous approach that provides strong privacy guarantees. In particular, differential privacy has several useful properties, including resistance to both postprocessing and the use of side information by adversaries. Although differential privacy was first proposed for static-database applications, this review focuses on its use in the context of control systems, in which the data under processing often take the form of data streams. Through two major applications—filtering and optimization algorithms—we illustrate the use of mathematical tools from control and optimization to convert a nonprivate algorithm to its private counterpart. These tools also enable us to quantify the trade-offs between privacy and system performance.

High-Level Design and Implementation of a Home Autonomous System Based on CPS Modeling

2018 ◽  
pp. 121-142
Author(s):  
Basman M. Alhafidh ◽  
William H. Allen
Keyword(s):  
Smart Home ◽  
Autonomous System ◽  
Machine Learning Algorithms ◽  
Design And Implementation ◽  
Use Of Resources ◽  
User Data ◽  
Level Design ◽  
High Level ◽  
Accuracy Of Prediction ◽  
User Actions

The process used to build an autonomous smart home system based on cyber-physical systems (CPS) principles has recently received increased attention from researchers and developers. However, there are many challenges to be resolved before designing and implementing such a system. In this chapter, the authors present a high-level design approach that simulates a smart home system by implementing three levels of the 5C architecture used in CPS modeling and uses well-known machine learning algorithms to predict future user actions. The simulation demonstrates how users will interact with the smart home system to make more efficient use of resources. The authors also present results from analyzing real-world user data to validate the accuracy of prediction of user actions. This research illustrates the benefits of considering CPS principles when designing a home autonomous system that reliably predicts a user's needs.

Cantor set based neighbor generation method for permutation solution representation

2020 ◽  
Vol 39 (5) ◽  
pp. 6157-6168
Author(s):  
Melike Öztürk ◽  
Çiğdem Alabaş-Uslu
Keyword(s):  
Quadratic Assignment Problem ◽  
Recursive Algorithm ◽  
Cantor Set ◽  
Quadratic Assignment ◽  
Single Solution ◽  
Efficiency And Effectiveness ◽  
Solution Representation ◽  
Incompatible Characteristics ◽  
Generation Mechanisms ◽  
The Traveling Salesman Problem

Metaheuristics gained world-wide popularity and researchers have been studying them vigorously in the last two decades. A relatively less explored approach in the improvement of metaheuristics is to design new neighbor generation mechanisms. Neighbor generation mechanisms are very important in the success of any single solution-based heuristic since they directly guide the search. In this study, a neighbor generation mechanism called cantor-set based (CB) method for single solution-based heuristics which use permutation solution representation is described. The inspiration for CB method stems from the recursive algorithm that constructs a cantor set which is a fractal set. Three variations of CB method are discussed (CB-1, CB-2, CB-3) considering the presented design possibilities. The computational experiments are conducted by embedding the mechanisms into the classical local search and simulated annealing algorithms, separately, to test their efficiency and effectiveness by comparing them to classical swap and insertion mechanisms. The traveling salesman problem (TSP) and quadratic assignment problem (QAP) which are very different problems that have incompatible characteristics have been chosen to test the mechanisms and sets of benchmark instances with varying sizes are chosen for the comparisons. The computational tests show that CB-2 gives very favorable results for TSP and CB-1 gives favorable results for QAP which means that CB-2 is suitable for problems that have steep landscapes and CB-1 is suitable for the problems that have flat landscapes. It is observed that CB-3 is a more generalized mechanism because it gives consistently good results for both TSP and QAP instances. The best mechanism for a given instance of the both problem types outperforms the classical neighbor generation of swap and insertion in terms of effectiveness.

scholarly journals A Simple and Approximately Optimal Mechanism for a Buyer with Complements

2020 ◽  
Author(s):  
Alon Eden ◽  
Michal Feldman ◽  
Ophir Friedler ◽  
Inbal Talgam-Cohen ◽  
S. Matthew Weinberg
Keyword(s):  
Real World ◽  
Recent Literature ◽  
Revenue Maximization ◽  
Trade Off ◽  
Optimal Mechanism

Recent literature on approximately optimal revenue maximization has shown that in settings where agent valuations for items are complement free, the better of selling the items separately and bundling them together guarantees a constant fraction of the optimal revenue. However, most real-world settings involve some degree of complementarity among items. The role that complementarity plays in the trade-off of simplicity versus optimality has been an obvious missing piece of the puzzle. In “A Simple and Approximately Optimal Mechanism for a Buyer with Complements,” the authors show that the same simple selling mechanism—the better of selling separately and as a grand bundle—guarantees a $\Theta(d)$ fraction of the optimal revenue, where $d$ is a measure of the degree of complementarity. One key modeling contribution is a tractable notion of “degree of complementarity” that admits meaningful results and insights—they demonstrate that previous definitions fall short in this regard.

scholarly journals Privacy-Preserving Monotonicity of Differential Privacy Mechanisms

2018 ◽  
Vol 8 (11) ◽  
pp. 2081 ◽  
Author(s):  
Hai Liu ◽  
Zhenqiang Wu ◽  
Yihui Zhou ◽  
Changgen Peng ◽  
Feng Tian ◽  
...  
Keyword(s):  
Differential Privacy ◽  
Estimation Error ◽  
Privacy Preserving ◽  
Randomized Response ◽  
Response Mechanism ◽  
Rational Model ◽  
Trade Off ◽  
Definition Of ◽  
Utility Metrics ◽  
Monotonicity Results

Differential privacy mechanisms can offer a trade-off between privacy and utility by using privacy metrics and utility metrics. The trade-off of differential privacy shows that one thing increases and another decreases in terms of privacy metrics and utility metrics. However, there is no unified trade-off measurement of differential privacy mechanisms. To this end, we proposed the definition of privacy-preserving monotonicity of differential privacy, which measured the trade-off between privacy and utility. First, to formulate the trade-off, we presented the definition of privacy-preserving monotonicity based on computational indistinguishability. Second, building on privacy metrics of the expected estimation error and entropy, we theoretically and numerically showed privacy-preserving monotonicity of Laplace mechanism, Gaussian mechanism, exponential mechanism, and randomized response mechanism. In addition, we also theoretically and numerically analyzed the utility monotonicity of these several differential privacy mechanisms based on utility metrics of modulus of characteristic function and variant of normalized entropy. Third, according to the privacy-preserving monotonicity of differential privacy, we presented a method to seek trade-off under a semi-honest model and analyzed a unilateral trade-off under a rational model. Therefore, privacy-preserving monotonicity can be used as a criterion to evaluate the trade-off between privacy and utility in differential privacy mechanisms under the semi-honest model. However, privacy-preserving monotonicity results in a unilateral trade-off of the rational model, which can lead to severe consequences.

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