第二回 PWS論文読破会

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2021/02/01(月) 本ページを作成

第3回開催決定

引き続き第3回を企画しました。是非ご参加ください。

開催概要

プライバシーに関する論文は、様々な分野の国際会議に発表されており関連技術も多数存在するため、プライバシーに関する技術動向を追うことは容易では無いと感じています。そこで、プライバシ関係の研究の促進を目的として、トップカンファレンスで発表されているプライバシ関係の論文を簡単に紹介し合う「PWS論文読破会」を企画しました。前回の第1回開催に引き続き、第2回目を開催したいと思います。

発表者・聴講者(特に、発表者)を募集しますので、下記の問い合わせ先にご連絡頂ければ幸いです。発表者の申し込みは、定員に達し次第締め切ります。

論文は2020年8月～2021年1月に発表されたプライバシ (秘密計算、差分プライバシ、匿名化、Federated Learning等)を中心にご自由にお選びください。また、プライバシと同様に注目されているデータ活用やAIにおける信頼性 (Safety, Fairness. Accountabilityなど) に関する論文も歓迎します。ご参考までに、NeurIPS2020 CCS2020 VLDB2020 KDD2020 からリストアップした論文リストを掲載します。(リストアップされていない論文も大歓迎です)

日時・場所

2021年3月24日(水) 15:00〜18:00(予定) ※終了後に希望者でオンライン飲み会もしたいと思います
オンライン Zoom (発表者・聴講者に別途URL等をご連絡しますので、申し込みをお願いします)
参加費無料

発表申込

発表を希望される方は、次のアドレスに電子メールをお送りください。

　　tsubasa.takahashi あっとまーく linecorp.com

ご記載いただきたい内容：

氏名
所属（会社名、学校名など）
メールアドレス
希望する論文

最新動向を追う事を目的としているため、比較的広く浅く論文を読んで、紹介し合うことを想定しています。そのため、必ずしも深く論文を読み綺麗な資料を作成する必要は無く、発表者の負荷はなるべく小さくしたいと考えております。

1論文につき10~15分程度（質疑込み）を想定しています。

聴講申込

聴講を希望される方は、申し込みフォームにメールアドレスなどを入力してください。後日、参加方法(Zoom URLなど)を連絡します。

なお、入力頂いた内容は本イベントの運営のために利用します。プライバシポリシはこちらを参照ください。

プログラム

15:00-15:05 オープニング
15:05-15:20 高橋翼 "Federated Evaluation and Tuning For On-device Personalization: System Design & Applications", https://arxiv.org/abs/2102.08503
15:20-15:35 リュウセンペイ "Inverting Gradients - How easy is it to break privacy in federated learning?", NeurIPS20 https://arxiv.org/abs/2003.14053
15:35-15:50 菊池浩明 "R2DP: A Universal and Automated Approach to Optimizing the Randomization Mechanisms of Differential Privacy for Utility Metrics with No Known Optimal Distributions", CCS20 https://arxiv.org/abs/2009.09451
15:50-16:00 休憩
16:00-16:15 荒井ひろみ "Privacy Norms for Smart Home Personal Assistants", CHI21
16:15-16:30 上野道彦 "Exploring Design and Governance Challenges in the Development of Privacy-Preserving Computation", CHI21 https://arxiv.org/abs/2101.08048
16:30-16:45 廣江彩乃 "Usage Patterns of Privacy-Enhancing Technologies", CCS20 https://arxiv.org/abs/2009.10278
16:45-17:00 休憩
17:00-17:15 松本茉倫 "Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data", NeurIPS20 https://arxiv.org/abs/1911.07101
17:15-17:30 渡辺知恵美 "ObliDB: Oblivious Query Processing for Secure Databases", VLDB20 https://arxiv.org/abs/1710.00458
17:30-17:45 竹之内隆夫 "Lessons and Challenges in Deploying (Heavy) MPC in Different Environments", RWC21
クロージング

代表的な国際会議論文リスト

KDD2020

Estimating Properties of Social Networks via Random Walk considering Private Nodes
Re-identification Attack to Privacy-Preserving Data Analysis with Noisy Sample-Mean
TIPRDC: Task-Independent Privacy-Respecting Data Crowdsourcing Framework for Deep Learning with Anonymized Intermediate Representations
Privileged Features Distillation at Taobao Recommendations
Faster Secure Data Mining via Distributed Homomorphic Encryption
Algorithmic Decision Making with Conditional Fairness
Evaluating Fairness using Permutation Tests
InFoRM: Individual Fairness on Graph Mining
List-wise Fairness Criterion for Point Processes
Towards Fair Truth Discovery from Biased Crowdsourced Answers
Attackability Characterization of Adversarial Evasion Attack on Discrete Data
Interpretability is a Kind of Safety: An Interpreter-based Ensemble for Adversary Defense
RayS: A Ray Searching Method for Hard-label Adversarial Attack
Vulnerability vs. Reliability: Disentangled Adversarial Examples for Cross-Modal Learning
INPREM: An Interpretable and Trustworthy Predictive Model for Healthcare

VLDB2020

Collecting and Analyzing Data Jointly from Multiple Services under Local Differential Privacy
Free Gap Information from the Differentially Private Sparse Vector and Noisy Max Mechanisms
A workload-adaptive mechanism for linear queries under local differential privacy
SAQE: Practical Privacy-Preserving Approximate Query Processing for Data Federations
ObliDB: Oblivious Query Processing for Secure Databases
Efficient Oblivious Database Joins
Set-valued Data Publication with Local Privacy: Tight Error Bounds and Efficient Mechanisms
Relational Data Synthesis using Generative Adversarial Networks: A Design Space Exploration
Secure Multi-Party Functional Dependency Discovery
TransNet: Training Privacy-Preserving Neural Network over Transformed Layer
Privacy Preserving Vertical Federated Learning for Tree-based Models
Efficient Confidentiality-Preserving Data Analytics over Symmetrically Encrypted Datasets
Rank Aggregation Algorithms for Fair Consensus
Fair Task Assignment in Spatial Crowdsourcing
Sieve: A Middleware Approach to Scalable Access Control for Database Management Systems
Understanding and Benchmarking the Impact of GDPR on Database Systems
Operationalizing Individual Fairness with Pairwise Fair Representations

CCS2020

Private Summation in the Multi-Message Shuffle Model
R^2DP: A Universal and Automated Approach to Optimizing the Randomization Mechanisms of Differential Privacy for Utility Metrics with No Known Optimal Distributions
Privaros: A Framework for Privacy-Compliant Delivery Drones
Implementing the Exponential Mechanism with Base-2 Differential Privacy
Usage Patterns of Privacy-Enhancing Technologies
CheckDP: An Automated and Integrated Approach for Proving Differential Privacy or Finding Precise Counterexamples
The Signal Private Group System and Anonymous Credentials Supporting Efficient Verifiable Encryption
Dangerous Skills Got Certified: Measuring the Trustworthiness of Skill Certification in Voice Personal Assistant Platforms
Threshold Password-Hardened Encryption Services
CrypTFlow2: Practical 2-Party Secure Inference

NeurIPS2020

Adversarially Robust Streaming Algorithms via Differential Privacy
Permute-and-Flip: A new mechanism for differentially private selection
Learning from Mixtures of Private and Public Populations
Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms
Optimal Private Median Estimation under Minimal Distributional Assumptions
Breaking the Communication-Privacy-Accuracy Trilemma
Differentially Private Clustering: Tight Approximation Ratios
Privacy Amplification via Random Check-Ins
Towards practical differentially private causal graph discovery
Differentially-Private Federated Linear Bandits
Synthetic Data Generators -- Sequential and Private
Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning
AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference
Smoothed Analysis of Online and Differentially Private Learning
Private Identity Testing for High-Dimensional Distributions
Locally Differentially Private (Contextual) Bandits Learning
GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators
Understanding Gradient Clipping in Private SGD: A Geometric Perspective
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity
Smoothly Bounding User Contributions in Differential Privacy
Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms
CoinPress: Practical Private Mean and Covariance Estimation
The Discrete Gaussian for Differential Privacy
On the Equivalence between Online and Private Learnability beyond Binary Classification
Inverting Gradients - How easy is it to break privacy in federated learning?
CryptoNAS: Private Inference on a ReLU Budget
The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space
Improving Sparse Vector Technique with Renyi Differential Privacy
A Computational Separation between Private Learning and Online Learning
Learning discrete distributions: user vs item-level privacy
Auditing Differentially Private Machine Learning: How Private is Private SGD?
Fairness without Demographics through Adversarially Reweighted Learning
Fairness with Overlapping Groups; a Probabilistic Perspective
Robust Optimization for Fairness with Noisy Protected Groups
Fair regression with Wasserstein barycenters
Learning Certified Individually Fair Representations
Fair Performance Metric Elicitation
Metric-Free Individual Fairness in Online Learning
Fairness constraints can help exact inference in structured prediction
Probabilistic Fair Clustering
Fairness in Streaming Submodular Maximization: Algorithms and Hardness
Group-Fair Online Allocation in Continuous Time
KFC: A Scalable Approximation Algorithm for k−center Fair Clustering
A Fair Classifier Using Kernel Density Estimation
Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning
Fair Multiple Decision Making Through Soft Interventions
Ensuring Fairness Beyond the Training Data
How do fair decisions fare in long-term qualification?
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference
Fair regression via plug-in estimator and recalibration with statistical guarantees
Fair Hierarchical Clustering
Falcon: Fast Spectral Inference on Encrypted Data
Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data
Optimal Query Complexity of Secure Stochastic Convex Optimization

お問い合わせ先

竹之内: takao-takenouchi あっとまーく garage.co.jp
高橋: tsubasa.takahashi　あっとまーく linecorp.com