Get To The Point: Summarization with Pointer-Generator Networks_acl17_論文紹介Masayoshi Kondo
Neural Text Summarizationタスクの研究論文.ACL'17- long paper採択.スタンフォード大のD.Manning-labの博士学生とGoogle Brainの共同研究.長文データ(multi-sentences)に対して、生成時のrepetitionを回避するような仕組みをモデルに導入し、長文の要約生成を可能とした.ゼミでの論文紹介資料.論文URL : https://arxiv.org/abs/1704.04368
Get To The Point: Summarization with Pointer-Generator Networks_acl17_論文紹介Masayoshi Kondo
Neural Text Summarizationタスクの研究論文.ACL'17- long paper採択.スタンフォード大のD.Manning-labの博士学生とGoogle Brainの共同研究.長文データ(multi-sentences)に対して、生成時のrepetitionを回避するような仕組みをモデルに導入し、長文の要約生成を可能とした.ゼミでの論文紹介資料.論文URL : https://arxiv.org/abs/1704.04368
Frequency-based Constraint Relaxation for Private Query Processing in Cloud D...Junpei Kawamoto
This document proposes a frequency-based constraint relaxation methodology for private queries in cloud databases. It aims to reduce computational costs for servers while maintaining privacy risks below existing "complete" protocols. The approach relaxes the constraint that servers must check all database items for a query by instead checking a subset, or "handled set", based on search intention frequencies. Evaluation on a real dataset found the approach reduces average query costs to 6.5% of complete protocols while keeping privacy risks comparable.
Securing Social Information from Query Analysis in Outsourced DatabasesJunpei Kawamoto
The document presents two methods for securing social information when databases are outsourced: Query Generalization by Dynamic Hash and Result Generalization by Bloom Filter. Queries are generalized through dynamic hashing to mix user queries and prevent determining relationships. Results are generalized using Bloom filters by including irrelevant tuples to mask which users requested the same tuples. The goal is to protect users' social information and relationships from being discovered by the outsourced database provider. Future work involves implementing and evaluating these methods on a real outsourced database service.
This document presents a study on privacy-preserving techniques for continually publishing location data. The authors propose a new definition of adversarial privacy that aims to preserve utility of published histograms while guaranteeing privacy against adversaries. They assume people's movements follow a Markov process and evaluate their approach on change point detection and frequent path extraction tasks, finding it achieves better utility than differential privacy while providing privacy guarantees.
A Locality Sensitive Hashing Filter for Encrypted Vector DatabasesJunpei Kawamoto
This document describes a locality sensitive hashing (LSH) filter for encrypted vector databases. The LSH filter allows cloud servers to filter database tuples without decrypting encrypted data, improving query efficiency. A whitening transformation is applied to encrypted vectors before inserting them into the LSH index to reduce skew in the vector space. When a query is received, the server computes the LSH of the encrypted query vector to identify candidate tuple groups for similarity checking based on the LSH hash values. Tuples with low estimated similarity are skipped, while those with high similarity have their actual encrypted similarity computed.
The document discusses how search engines can sell advertising tied to search queries through an auction-based system. It describes how search queries express a user's intent, allowing targeted advertising. The document proposes using a Vickrey-Clarke-Groves auction to encourage truthful bidding by advertisers by having the winner pay an amount equal to the harm caused to other bidders. This system maximizes total advertiser valuations and incentives truthful reporting of private valuations.
Private Range Query by Perturbation and Matrix Based EncryptionJunpei Kawamoto
This document proposes a new method called Inner Product Predicate (IPP) for performing private range queries over encrypted data. The IPP method adds perturbations to attribute values and queries through matrix-based encryption to prevent frequency analysis attacks. Experimental results show the transformed query distributions are different from the originals and query processing time is linear in the number of tuples. Open problems remain around reducing computational costs and defending against attacks using aggregate query results.
VLDB2009のSession27より,
1) Anonymization of Set-Valued Data via Top-Down, Local Generalization (He and Naughton)
2) K-Automorphism: A General Framework For Privacy Preserving Network Publication (Zou, Chen, and Özsu)
3) Distribution-based Microdata Anonymization (Koudas, Srivastava, Yu, Zhang)
を簡単に紹介.
VLDB2009勉強会: http://qwik.jp/vldb2009-study/
Reducing Data Decryption Cost by Broadcast Encryption and Account Assignment ...Junpei Kawamoto
The document proposes two methods for reducing data decryption costs while preserving social information for web applications. The naive method encrypts access control lists but results in long key chains and high decryption costs. The proposed method uses broadcast encryption to encrypt authority information in one encryption and account assignment to reduce decryption candidates, lowering costs and improving precision. Simulation results show the proposed method maintains near-perfect precision independent of user numbers while requiring only one decryption.
Security of Social Information from Query Analysis in DaaSJunpei Kawamoto
This document discusses protecting social information inferred from database queries. It introduces the problem of attackers extracting social relationships between users from query logs. An attack model is presented where users querying similar topics are assumed to be related. The document then proposes a method of query conversion using an extendible hashing tree to map original queries to generalized queries. This aims to prevent determining the exact original queries and therefore the social relationships between users from the query logs. Evaluation of the method shows it can significantly reduce the precision and recall of the attack in determining social information.
Frequency-based Constraint Relaxation for Private Query Processing in Cloud D...Junpei Kawamoto
This document proposes a frequency-based constraint relaxation methodology for private queries in cloud databases. It aims to reduce computational costs for servers while maintaining privacy risks below existing "complete" protocols. The approach relaxes the constraint that servers must check all database items for a query by instead checking a subset, or "handled set", based on search intention frequencies. Evaluation on a real dataset found the approach reduces average query costs to 6.5% of complete protocols while keeping privacy risks comparable.
Securing Social Information from Query Analysis in Outsourced DatabasesJunpei Kawamoto
The document presents two methods for securing social information when databases are outsourced: Query Generalization by Dynamic Hash and Result Generalization by Bloom Filter. Queries are generalized through dynamic hashing to mix user queries and prevent determining relationships. Results are generalized using Bloom filters by including irrelevant tuples to mask which users requested the same tuples. The goal is to protect users' social information and relationships from being discovered by the outsourced database provider. Future work involves implementing and evaluating these methods on a real outsourced database service.
This document presents a study on privacy-preserving techniques for continually publishing location data. The authors propose a new definition of adversarial privacy that aims to preserve utility of published histograms while guaranteeing privacy against adversaries. They assume people's movements follow a Markov process and evaluate their approach on change point detection and frequent path extraction tasks, finding it achieves better utility than differential privacy while providing privacy guarantees.
A Locality Sensitive Hashing Filter for Encrypted Vector DatabasesJunpei Kawamoto
This document describes a locality sensitive hashing (LSH) filter for encrypted vector databases. The LSH filter allows cloud servers to filter database tuples without decrypting encrypted data, improving query efficiency. A whitening transformation is applied to encrypted vectors before inserting them into the LSH index to reduce skew in the vector space. When a query is received, the server computes the LSH of the encrypted query vector to identify candidate tuple groups for similarity checking based on the LSH hash values. Tuples with low estimated similarity are skipped, while those with high similarity have their actual encrypted similarity computed.
The document discusses how search engines can sell advertising tied to search queries through an auction-based system. It describes how search queries express a user's intent, allowing targeted advertising. The document proposes using a Vickrey-Clarke-Groves auction to encourage truthful bidding by advertisers by having the winner pay an amount equal to the harm caused to other bidders. This system maximizes total advertiser valuations and incentives truthful reporting of private valuations.
Private Range Query by Perturbation and Matrix Based EncryptionJunpei Kawamoto
This document proposes a new method called Inner Product Predicate (IPP) for performing private range queries over encrypted data. The IPP method adds perturbations to attribute values and queries through matrix-based encryption to prevent frequency analysis attacks. Experimental results show the transformed query distributions are different from the originals and query processing time is linear in the number of tuples. Open problems remain around reducing computational costs and defending against attacks using aggregate query results.
VLDB2009のSession27より,
1) Anonymization of Set-Valued Data via Top-Down, Local Generalization (He and Naughton)
2) K-Automorphism: A General Framework For Privacy Preserving Network Publication (Zou, Chen, and Özsu)
3) Distribution-based Microdata Anonymization (Koudas, Srivastava, Yu, Zhang)
を簡単に紹介.
VLDB2009勉強会: http://qwik.jp/vldb2009-study/
Reducing Data Decryption Cost by Broadcast Encryption and Account Assignment ...Junpei Kawamoto
The document proposes two methods for reducing data decryption costs while preserving social information for web applications. The naive method encrypts access control lists but results in long key chains and high decryption costs. The proposed method uses broadcast encryption to encrypt authority information in one encryption and account assignment to reduce decryption candidates, lowering costs and improving precision. Simulation results show the proposed method maintains near-perfect precision independent of user numbers while requiring only one decryption.
Security of Social Information from Query Analysis in DaaSJunpei Kawamoto
This document discusses protecting social information inferred from database queries. It introduces the problem of attackers extracting social relationships between users from query logs. An attack model is presented where users querying similar topics are assumed to be related. The document then proposes a method of query conversion using an extendible hashing tree to map original queries to generalized queries. This aims to prevent determining the exact original queries and therefore the social relationships between users from the query logs. Evaluation of the method shows it can significantly reduce the precision and recall of the attack in determining social information.
16. 2013/1/23 プライバシを考慮した移動系列情報解析のための安全性の提案 16
プライバシと経過時間の関係
• 攻撃者が対象の位置を観測してからの経過時間 s
• s が小さい時、s 時間で到達可能な範囲は小さい
• s が大きい時、到達可能範囲は広く可能な経路も複雑になる
s が大きいほどヒストグラムによるプライバシの侵害度合いは大きい
s = 3 で到達可能な範囲 s = 7 で到達可能な範囲