Breaking Anonymity of Social Network Accounts by Using Coordinated and Extensible Classifiers Based on Machine Learning

Part 5: Impression, Trust, and Risk ManagementInternational audienceA method for de-anonymizing social network accounts is presented to clarify the privacy risks of such accounts as well as to deter their misuse such as by posting copyrighted, offensive, or bullying contents. In contrast to previous de-anonymization methods, which link accounts to other accounts, the presented method links accounts to resumes, which directly represent identities. The difficulty in using machine learning for de-anonymization, i.e. preparing positive examples of training data, is overcome by decomposing the learning problem into subproblems for which training data can be harvested from the Internet. Evaluation using 3 learning algorithms, 2 kinds of sentence features, 238 learned classifiers, 2 methods for fusing scores from the classifiers, and 30 volunteers’ accounts and resumes demonstrated that the proposed method is effective. Because the training data are harvested from the Internet, the more information that is available on the Internet, the greater the effectiveness of the presented method

Analysis of Modified Franz-Keldysh Effect under Influence of Electronic Intraband Relaxation Phenomena

金沢大学工学部電子情報工学科／Department of Electrical and Computer Engineering, Faculty of Engineering, Kanazawa UniversityCharacteristics of the Franz-Keldysh effect were theoretically analyzed by taking into account electronic intraband relaxation. Theoretical analysis of optical absorption was performed basing on the density matrix formalism with the help of a stochastic model to include the intraband relaxation. The absorption tails observed in experiments were well explained by this theoretical analysis, together with the Franz-Keldysh effect itself.Embargo Period 12 month

Observation of optical amplification excited by traveling electron beam

金沢大学工学部電子情報工学科／Department of Electrical and Computer Engineering, Faculty of Engineering, Kanazawa UniversityThe amplification of optical light excited by a traveling electron in a vacuum environment was first observed in the near-infrared optical region as that in a Cherenkov-type amplifier. Light with a wavelength of 1.5 µm propagated in a Si–SiO2 dielectric waveguide. The accelerated voltage of the electron beam for the amplification was approximately 42 kV. The dispersion relation between wavelength and acceleration voltage well agreed with theoretically calculated results.Embargo Period 12 month

機械学習を用いたソーシャルネットワークと履歴書の照合方式の提案

ソーシャルネットワークのプライバシリスクの明確化および悪用の抑止のために，匿名のソーシャルネットワークアカウントを組織が保有する履歴書と照合する手法を述べる．提案手法は機械学習を用い，履歴書に含まれる性別，趣味などの属性値ごとに，ソーシャルネットワークアカウントの投稿文が当該属性値を持つ人によって書かれたものかを判定する．この属性値ごとの識別器を組み合わせることにより，ソーシャルネットワークの投稿文が履歴書の当人によって書かれたものであるかを判定する．機械学習のための訓練データはソーシャルネットワーク上の他のアカウントから収集する．30人の被験者のソーシャルネットワークアカウントと履歴書を用い，投稿文の特徴量を2種類，機械学習アルゴリズムを5種類，履歴書中の着目する属性群3セット，属性ごとのスコアの統合方法2種類により，提案手法を評価した．その結果，最良ケースにおいて，30アカウント中5アカウントは本人の履歴書と正しく照合でき，14アカウントは30人中3人に絞り込むことができ，19アカウントは6人に絞り込むことができた．This paper describes a method that links anonymous accounts of social networks to resumes held by organizations. Using machine learning, the proposed method generates a classifier for each attribute value described in each resume, such as gender of female and hobby of dancing. It uses each classifier to judge posts in an account were written by a person who has such an attribute value. By combining scores from these resumes, the method judges the posts were written by a person of the resume. Training data for machine learning are collected from other accounts of the social network. The proposed method was evaluated by using 30 pairs of accounts and resumes with 2 kinds of sentence feature, 5 machine learning algorithms, 3 sets of resume attributes, and two methods of score fusion. In the best combination of parameters, the correct resumes were identified for 5 accounts, they were in 3 identified resumes for 14 accounts and in 6 identified resumes for 19 accounts

機械学習を用いたソーシャルネットワークと履歴書の照合方式の提案

ソーシャルネットワークのプライバシリスクの明確化および悪用の抑止のために，匿名のソーシャルネットワークアカウントを組織が保有する履歴書と照合する手法を述べる．提案手法は機械学習を用い，履歴書に含まれる性別，趣味などの属性値ごとに，ソーシャルネットワークアカウントの投稿文が当該属性値を持つ人によって書かれたものかを判定する．この属性値ごとの識別器を組み合わせることにより，ソーシャルネットワークの投稿文が履歴書の当人によって書かれたものであるかを判定する．機械学習のための訓練データはソーシャルネットワーク上の他のアカウントから収集する．30人の被験者のソーシャルネットワークアカウントと履歴書を用い，投稿文の特徴量を2種類，機械学習アルゴリズムを5種類，履歴書中の着目する属性群3セット，属性ごとのスコアの統合方法2種類により，提案手法を評価した．その結果，最良ケースにおいて，30アカウント中5アカウントは本人の履歴書と正しく照合でき，14アカウントは30人中3人に絞り込むことができ，19アカウントは6人に絞り込むことができた．This paper describes a method that links anonymous accounts of social networks to resumes held by organizations. Using machine learning, the proposed method generates a classifier for each attribute value described in each resume, such as gender of female and hobby of dancing. It uses each classifier to judge posts in an account were written by a person who has such an attribute value. By combining scores from these resumes, the method judges the posts were written by a person of the resume. Training data for machine learning are collected from other accounts of the social network. The proposed method was evaluated by using 30 pairs of accounts and resumes with 2 kinds of sentence feature, 5 machine learning algorithms, 3 sets of resume attributes, and two methods of score fusion. In the best combination of parameters, the correct resumes were identified for 5 accounts, they were in 3 identified resumes for 14 accounts and in 6 identified resumes for 19 accounts