鐃緒申<< 鐃重種申鐃緒申離鐃緒申沺鐃緒申肇妊丱鐃緒申鐃緒申鐃緒申鐃塾�2016 鐃緒申鐃緒申鐃塾ブワ申鐃緒申鐃塾トップわ申鐃緒申鐃緒申鐃夙わ申労働鐃粛醐申 >>

2016年12鐃緒申28鐃緒申

鐃緒申鐃緒申鐃舜緒申鐃緒申妊鐃緒申鐃緒申蠅刻申鐃緒申鐃祝�

 このエントリーをはてなブックマークに追加
follow us in feedly
鐃叔駈申漏惱鐃緒申僂澆竜鐃緒申鐃緒申惱鐃緒申鐃叔ワ申鐃緒申鐃緒申鐃緒申薀�鐃緒申鐃緒申鐃緒申鐃叔わ申鐃緒申鐃緒申屬砲鐃緒申討鐃緒申襯居申鐃緒申咼鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申鐃銃わ申鐃殉わ申鐃緒申鐃緒申
鐃緒申鐃緒申鐃夙種申鐃緒申鐃祝関わ申鐃暑研鐃緒申發�鐃緒申泙鐃緒申鐃�




Stealing Machine Learning Models via Prediction APIs
Florian Tramer 鐃緒申鐃緒申EPFL
Fan Zhang鐃緒申鐃緒申Cornell University
Ari Juels鐃緒申鐃緒申Cornell Tech, Jacobs Institute
Michael K. Reiter鐃緒申鐃緒申UNC Chapel Hill
Thomas Ristenpart鐃緒申鐃緒申Cornell Tech
USENIX Security. 2016.


鐃緒申鐃緒申鐃舜緒申鐃緒申妊鐃熟居申密鐃緒申鐃緒申里茲�鐃緒申物鐃叔わ申鐃暑が鐃緒申鐃緒申妊鐃塾居申動鐃緒申鐃緒申仆鐃緒申鐃暑か鐃盾し鐃緒申覆鐃緒申鐃�
鐃緒申鐃緒申呂匹里茲�鐃淑刻申鐃緒申鐃緒申法鐃緒申鐃夙醐申鐃緒申鐃夙￥申鐃瞬ワ申奪鐃緒申椒奪鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申任鐃緒申辰討癲�鐃舜緒申鐃緒申妊鐃塾パワ申瓠種申鐃緒申泙鐃緒申魯如鐃緒申鐃緒申亡悗鐃緒申鐃緒申鐃緒申鐃緒申鐃塾種申鐃緒申鐃淑わ申鐃緒申鐃緒申圓魯鐃叔ワ申竜鐃叔緒申鐃淑ｏ申鐃緒申鐃緒申鐃淑わ申鐃淑わ申鐃緒申鐃緒申鐃緒申鐃緒申廖砲鐃緒申箸鐃緒申鐃重�鐃夙わ申鐃暑。
鐃緒申典的鐃舜緒申鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申箸楼曚覆鐃�鐃緒申鐃緒申鐃緒申鐃塾ワ申鐃緒申鐃俊ワ申鐃叔は￥申鐃緒申徴鐃駿ワ申鐃夙ワ申鐃緒申鐃緒申呂箸鐃緒申銅鐃緒申鐃緒申鐃所、予測鐃粛わ申泙狄�鐃緒申鐃粛わ申鐃瞬わ申鐃銃わ申鐃暑。

鐃緒申鐃緒申鐃銃э申泙鐃緒申鐃出￥申鐃緒申鐃緒申鐃緒申鐃銃ワ申鐃獣ワ申鐃藷帰￥申鐃祝ュー鐃緒申鐃粛ットワー鐃緒申鐃緒申鐃緒申鐃緒申鐃准などの逸申鐃緒申的鐃淑ワ申妊襯�鐃初ス鐃祝はほぼ器申鐃緒申鐃淑学緒申鐃緒申妊鐃緒申鐃緒申仆鐃緒申鐃緒申鐃塾�的鐃淑刻申鐃緒申鐃緒申法鐃緒申鐃緒申討鐃緒申襪鰹申箸鐃緒申鐃緒申鐃暑。
BigML鐃緒申Amazon Machine Learning鐃塾ワ申鐃緒申薀わ申鵐機鐃緒申咼鐃緒申鐃緒申个鐃緒申襪鰹申鐃緒申旅鐃緒申鐃緒申他擇鐃緒申鐃緒申鐃�
鐃緒申鐃緒申法鐃�鐃緒申妊鐃緒申鐃熟わ申鐃初信鐃緒申鐃粛わ申鐃塾�鐃緒申鐃緒申箸鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申从鐃緒申任鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申箸鐃緒申鐃緒申鐃緒申鐃重�鐃緒申有鐃緒申鐃淑ワ申妊鐃緒申鐃出刻申鐃緒申鐃緒申鐃緒申鐃緒申

鐃緒申鐃緒申文鐃叔は￥申鐃舜緒申鐃緒申妊鐃緒申鐃緒申鐃瞬と￥申鐃緒申鐃緒申鐃緒申鐃緒申妊鐃緒申鐃緒申鐃出削申鐃緒申必鐃緒申鐃緒申鐃緒申調鐃緒申鐃銃わ申鐃緒申





Machine learning services.
A number of companies have launched or are planning to launch cloud-based ML services.
A common denominator is the ability of users to upload data sets, have the provider run training algorithms on the data, and make the resulting models generally available for prediction queries.

table2




Service White-box Monetize Confidence Scores Logistic Regression SVM Neural Network Decision Tree 鐃緒申
Amazon x x 鐃緒申 鐃緒申 x x x 鐃緒申
Microsoft x x 鐃緒申 鐃緒申 鐃緒申 鐃緒申 鐃緒申 鐃緒申
BigML 鐃緒申 鐃緒申 鐃緒申 鐃緒申 x x 鐃緒申 鐃緒申
PredictionIO 鐃緒申 x x 鐃緒申 鐃緒申 x 鐃緒申 鐃緒申
Google x 鐃緒申 鐃緒申 鐃緒申 鐃緒申 鐃緒申 鐃緒申 鐃緒申

鐃緒申White-box鐃竣とは￥申鐃緒申鐃緒申鐃緒申鐃曙た鐃緒申妊鐃緒申鐃緒申鐃緒申鐃緒申鐃叔ワ申鐃緒申鐃緒申鐃緒申鐃緒申匹鐃緒申道鐃緒申僂鐃緒申襪鰹申鐃�
鐃緒申Monetize鐃竣とは￥申鐃醇ー鐃緒申鐃緒申鐃緒申鐃緒申妊鐃舜のブワ申奪鐃緒申椒奪鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申鐃渋常申離罅種申鐃緒申鐃緒申鵬欟發刻申襪鰹申鐃�




4 Extraction with Confidence Values

鐃殉わ申鐃緒申鐃緒申鐃緒申鐃緒申鐃粛わ申鐃瞬わ申予測API鐃緒申鐃緒申鐃旬わ申鐃緒申鐃緒申亶鐃緒申鐃緒申鐃祝�
鐃緒申鐃緒申鐃叔は￥申logistic regressions (LR), neural networks, and decision trees鐃緒申鐃緒申鐃出象とわ申鐃銃わ申鐃暑。
4.1 Equation-Solving Attacks

多鐃緒申鐃塾居申鐃緒申鐃舜緒申鐃緒申妊鐃熟￥申鐃緒申鐃緒申x鐃夙実随申鐃粛ワ申妊鐃術ワ申瓠種申鐃緒申鐃熟�続鐃舜随申鐃夙わ申鐃銃ワ申鐃初ス鐃緒申率鐃緒申彁鐃緒申鐃緒申討鐃緒申鐃�
鐃緒申鐃塾常申隋�鐃緒申鐃塾ワ申鐃初ス鐃緒申率鐃薯示わ申API鐃熟￥申未鐃塾ワ申妊鐃術ワ申瓠種申鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申箸鐃緒申童鐃緒申襪鰹申箸鐃緒申任鐃緒申鐃�
鐃緒申鐃緒申廛鐃� (x, f(x)) 鐃緒申敵鐃緒申鐃藷供わ申鐃緒申鐃緒申砲覆鐃�
4.1.1 Binary logistic regression


鐃緒申単鐃淑緒申発鐃緒申鐃夙わ申鐃銃￥申鐃緒申鐃緒申鐃緒申鐃銃ワ申鐃獣ワ申鐃藷帰￥申LR鐃祝のワ申鐃緒申鐃緒申鐃緒申討鐃緒申鐃緒申
LR鐃熟わ申鐃塾ワ申鐃緒申廛襪居申噺鐃塾�鐃緒申鐃塾わ申鐃緒申任鐃緒申鐃緒申的鐃緒申鐃緒申鐃緒申分鐃緒申鐃塾逸申弔任鐃緒申蝓�多鐃緒申鐃緒申分鐃緒申嚢鐃緒申鐃緒申鐃緒申僂鐃緒申鐃銃わ申鐃所、table2鐃緒申鐃銃のワ申鐃緒申鐃俊ワ申鐃叔ワ申鐃楯￥申鐃夙わ申鐃緒申討鐃緒申襦�


鐃緒申鐃緒申鐃緒申鐃銃ワ申鐃獣ワ申鐃藷帰わ申
stealing

鐃叔わ申鐃暑か鐃初、
w 鐃緒申鐃緒申哘造鐃緒申鐃緒申鐃緒申襪随申鐃祝は￥申d + 1鐃縦のワ申鐃緒申廛襪�必鐃竣緒申分鐃叔わ申鐃緒申鐃�x鐃緒申鐃緒申鐃緒申鐃緒申立鐃叔わ申鐃緒申鐃緒申鐃�
鐃緒申鐃緒申狼鐃緒申鐃緒申惱鐃緒申鐃叔ルサ鐃緒申鐃俊ワ申鐃舜わ申単鐃緒申離丱奪鐃緒申弋鐃淑わ申鐃緒申鐃祝わ申鐃緒申鐃暑こ鐃夙わ申鐃緒申鐃緒申襦�
鐃緒申鐃緒申鐃緒申亶鐃緒申鐃緒申鐃緒申鐃緒申単鐃緒申鐃緒申鐃緒申鐃緒申鐃銃わ申鐃緒申鐃緒申鐃緒申討離鐃緒申薀�鐃宿ベ￥申鐃緒申鐃塾ワ申鐃緒申鐃俊ワ申鐃祝￥申鐃緒申命的鐃淑件申未鐃盾た鐃初す鐃緒申能鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申

鐃緒申妊觜緒申朿惱鐃緒申虜櫃蓮淵如鐃緒申鐃�D鐃緒申 D>>d 鐃緒申鐃順規鐃熟デ￥申鐃緒申鐃緒申箸辰導惱鐃緒申鐃緒申鐃塾わ申
鐃緒ず鐃緒申d+1鐃緒申離鐃緒申鐃緒申鐃緒申鐃緒申妊鐃叔ワ申鐃銃醐申鐃叔わ申鐃緒申鐃緒申鐃塾ワ申妊鐃熟課駈申鐃叔ワ申任鐃緒申鐃緒申鐃盾あ鐃緒申里如鐃緒申鐃緒申鐃緒申鐃緒申払鐃獣てでわ申鐃叔ワ申鐃緒申鐃緒申鐃緒申佑呂鐃緒申襦�
鐃緒申鐃竣なのは￥申鐃緒申丱鐃緒申鐃緒申箸鐃�| D |鐃夙わ申鐃緒申立鐃緒申鐃銃わ申鐃暑こ鐃夙でわ申鐃緒申






4.1.2 Multiclass LRs and Multilayer Perceptrons


鐃緒申鐃塾刻申鐃緒申蓮鐃緒申泪鐃緒申鐃緒申鐃初ス鐃緒申c> 2鐃祝わ申LR鐃巡、
Deep neural network鐃緒申泙鐃瞬ワ申鐃緒申鐃緒申鐃銃ワ申鐃獣ワ申鐃竣レイ鐃巡ー鐃緒申鐃緒申弔鐃緒申戮討離鐃叔ルク鐃初ス鐃祝刻申鐃緒申適鐃術緒申鐃緒申襦�

multiclass logistic regression鐃緒申MLR鐃祝は￥申鐃緒申鐃曙ぞ鐃曙が鐃術ワ申瓠種申鐃�wi bi鐃緒申有鐃緒申鐃緒申c鐃出ワ申鐃淑ワ申鐃叔ワ申鐃緒申箸濆鐃緒せ鐃銃マワ申鐃緒申鐃緒申薀刻申鐃叔ワ申鐃緒申鐃緒申鐃緒申鐃暑。
鐃緒申討鐃緒申鐃緒申鐃緒申鐃駿てのワ申鐃緒申鐃俊ワ申鐃緒申MLR鐃緒申鐃緒申鐃術駕申能鐃叔わ申鐃獣わ申鐃緒申

.......................

鐃緒申鐃緒申鐃緒申法鐃緒申鐃夙み刻申錣誌申砲蓮鐃�softmax 鐃緒申 one-vs-rest (OvR)鐃緒申鐃緒申鐃緒申爐�鐃緒申鐃緒申

鐃緒申鐃春トマッワ申鐃緒申鐃叔は￥申鐃緒申鐃銃の学緒申鐃叔￥申鐃緒申鐃緒申連鐃緒申多鐃緒申分鐃循わ申適鐃順さ鐃緒申鐃暑。
OvR鐃緒申妊鐃熟￥申鐃銃ワ申鐃初ス鐃緒申鐃出わ申鐃緒申鐃縮個わ申LR鐃緒申惱鐃緒申鐃緒申鐃緒申鐃緒申薀刻申鐃塾�鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申鐃暑。

A common method for solving such a system is by minimizing an appropriate loss function, such as the logistic loss.
With a regularization term, the loss function is strongly convex, and the optimization thus converges to a global minimum (i.e., a function ^f that predicts the same probabilities as f for all available samples).
A similar optimization (over class labels rather than probabilities) is actually used for training logistic models. Any MLR implementation can thus easily be adapted for model extraction with equation-solving.

鐃緒申鐃塾ワ申鐃竣ワ申鐃緒申鐃緒申鐃熟種申鐃緒申鐃緒申deep鐃祝ュー鐃緒申鐃粛ットワー鐃緒申鐃祝わ申鐃縦ワ申任鐃緒申襦�
鐃殉わ申鐃緒申鐃緒申鐃緒申鐃緒申鐃術器申鐃薯すべてわ申鐃緒申鐃熟￥申hidden layer鐃祝わ申適鐃術わ申鐃暑。
鐃緒申鐃緒申鐃緒申鐃術器申鐃緒申鐃曙た鐃緒申鐃瞬わ申softmax鐃藷帰わ申適鐃術わ申鐃緒申多鐃舜パ￥申鐃緒申鐃竣トワ申鐃緒申鐃�MLP鐃祝わ申佑鐃緒申襦�











鐃緒申亶鐃緒申鐃塾種申鐃緒申箸鐃緒申討鐃�Amazon Machine Learning鐃緒申鐃緒申

5 Online Model Extraction Attacks
5.2 Case Study 2: Amazon Web Services

Amazon Machine Learning鐃叔はワ申鐃緒申鐃緒申鐃銃ワ申鐃獣ワ申鐃藷帰わ申箸辰討鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申鐃銃わ申鐃緒申里鐃�鐃緒申
https://aws.amazon.com/jp/machine-learning/details/
Amazon Machine Learning 鐃叔は￥申鐃緒申鐃緒申鐃緒申鐃緒申屮鐃叔件申牢鐃淑業鰹申標鐃緒申鐃� ML 鐃緒申鐃暑ゴ鐃所ズ鐃緒申亮鐃緒申鐃緒申鐃緒申鐃術わ申鐃銃わ申鐃殉わ申鐃緒申Amazon Machine Learning 鐃緒申鐃緒申僂鐃緒申鐃夙￥申鐃緒申発鐃峻は￥申鐃出ワ申鐃淑ワ申属鐃緒申鐃淑バワ申鐃淑ワ申分鐃緒申法鐃緒申鐃緒申謄鐃緒申鐃渋逸申鐃緒申鐃淑ｏ申鐃緒申鐃緒申薀絞�鐃緒申法鐃�鐃緒申鐃緒申属鐃緒申鐃淑回帰￥申鐃緒申鐃粛わ申予測鐃緒申鐃緒申鐃叔ワ申鐃緒申鐃緒申鐃叔わ申鐃殉わ申鐃緒申鐃緒申鐃夙わ申鐃出￥申鐃出ワ申鐃淑ワ申分鐃緒申鐃叔ワ申鐃緒申鐃術わ申鐃銃ワ申鐃緒申鐃瞬ワ申鐃緒申鐃夙のワ申鐃緒申鐃夙わ申鐃緒申鐃術ワ申任鐃緒申襪�鐃宿わ申鐃緒申鐃淑「はわ申鐃竣まわ申鐃熟「わ申鐃緒申鐃緒申鐃竣など）わ申予測鐃叔わ申鐃殉わ申鐃緒申複鐃緒申鐃緒申鐃初ス分鐃緒申鐃叔ワ申鐃緒申鐃術わ申鐃緒申函鐃緒申鐃緒申鐃緒申鐃緒申沺鐃緒申鐃緒申鐃緒申咼鐃緒申離螢�鐃緒申鐃緒申鐃夙（￥申鐃緒申鐃緒申廖鐃緒申峙鐃緒申僖鐃緒申檗鐃緒申函廖鐃緒申鐃緒申鐃淑醐申鐃緒申董鐃緒申鐃緒申鐃緒申廚覆鼻砲鐃重常申鐃緒申鐃緒申予測鐃叔わ申鐃殉わ申鐃緒申鐃藷帰ワ申妊鐃�鐃緒申鐃緒申僂鐃緒申鐃夙￥申鐃緒申鐃竣リケ鐃緒申鐃緒申鐃緒申鐃殉わ申鐃熟ワ申鐃緒申鐃俊ワ申鐃祝関わ申鐃暑お鐃緒申鐃粛わ申鐃緒申亮鐃緒申鐃緒申笋わ申鐃緒せ鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申鐃暑か鐃緒申予測鐃叔わ申鐃殉わ申鐃緒申
4.1 鐃緒申鐃緒申法鐃叔￥申鐃緒申妊鐃緒申童鐃緒申任鐃緒申襦�

................................
................................

7 Extraction Countermeasures


鐃緒申鐃緒申鐃殉でに種申鐃緒申鐃緒申鐃処う鐃祝居申鐃緒申鐃舜緒申鐃緒申妊鐃熟ブワ申奪鐃緒申椒奪鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申任鐃緒申鐃淳出わ申鐃緒申鐃夙わ申鐃緒申能鐃叔わ申鐃暑。

................................

鐃緒申鐃緒申分鐃緒申鐃緒申討鐃緒申鐃緒申標鐃祝￥申蓮鐃緒申鐃緒申鐃緒申戮鐃緒申鐃緒申鐃緒申鐃駿に四捨醐申鐃緒申鐃緒申鐃緒申鐃�
鐃緒申鐃駿わ申鐃緒申鐃渋わ申鐃緒申函鐃�
鐃緒申鐃緒申鐃緒申鐃緒申法鐃緒申鐃緒申両鐃順、
鐃緒申鐃緒申譴随申鐃縮は￥申鐃緒申標f鐃叔はなわ申鐃緒申鐃緒申鐃塾逸申鐃緒申鐃緒申鐃准わ申里討鐃淑�鐃祝なるか鐃緒申

鐃舜緒申鐃緒申妊鐃緒申鐃楯護す鐃暑た鐃緒申離瓮�鐃祝ワ申鐃緒申箸鐃緒申董鐃�
Differential privacy (DP) [DWORK, C. Differential privacy. In ICALP (2006)] 鐃夙わ申鐃緒申鐃術種申
[LI, N., QARDAJI, W., SU, D., WU, Y., AND YANG, W. Membership privacy: A unifying framework for privacy definitions. In CCS (2013), ACM.]
鐃緒申鐃緒申討鐃緒申鐃銃わ申鐃暑。

鐃緒申鐃藷サワ申屮鐃緒申法鐃薯タ￥申鐃緒申鐃獣トとわ申鐃銃実醐申鐃緒申鐃銃わ申鐃殉わ申鐃藷が￥申鐃緒申鐃緒申圓魯鐃緒申鐃緒申鐃緒申奪抜愎鐃緒申鐃緒申鐃緒申的鐃銃わ申鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申襪鰹申箸鐃緒申任鐃緒申覆鐃緒申箸鐃緒申鐃緒申鐃縮ｏ申如鐃緒申鐃出刻申鐃緒申鐃緒申个鐃緒申討鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申塙佑鐃緒申鐃緒申泙鐃緒申鐃�
鐃緒申鐃緒申砲發�鐃緒申鐃緒申蕕削申鐃緒申鐃緒申鵐汽鐃瞬ワ申鐃祝￥申魯鐃叔ワ申鐃緒申鐃淑どわ申他鐃塾刻申鐃緒申鐃緒申个鐃緒申鐃緒申伴鐃叔わ申鐃緒申鐃叔緒申鐃緒申鐃緒申鐃緒申襦�
................................





tak_tak0 at 22:01鐃緒申鐃緒申鐃緒申鐃緒申(0)鐃緒申鐃夙ワ申奪鐃緒申丱奪鐃�(0) 鐃緒申鐃緒申鐃緒申   この記事をクリップ！

鐃夙ワ申奪鐃緒申丱奪鐃�URL

鐃緒申鐃緒申鐃夙わ申鐃緒申

名鐃緒申:
URL:
  鐃緒申鐃緒申魑�駕申鐃緒申 評鐃緒申鐃緒申  鐃緒申   鐃緒申
 
 
 
鐃緒申鐃緒申鐃緒申鐃盾検鐃緒申
にほんブログ村 科学ブログへ
鐃祝ほわ申屮鐃緒申鐃渋�
adsense
Archives
amazon
鐃叔随申鐃緒申鐃緒申
nepenthes鐃塾種申鐃緒申鐃緒申法
s1-32B 鐃塾種申鐃緒申鐃緒申法
鐃緒申鐃緒申鐃緒申鐃瞬わ申敵鐃緒申的鐃緒申牢鐃緒申鐃塾トレー鐃宿ワ申鐃緒申
鐃銃ワ申鐃夙誌申鐃瞬ワ申鐃緒申鐃緒申鐃緒申鵐阿鐃緒申鐃緒申鐃�OpenAI o1鐃緒申鐃緒申鐃緒申妊鐃緒申鐃循わ申鐃緒申
鐃緒申鐃緒申AI鐃緒申存鐃塾学緒申動鐃緒申鐃緒申廛鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申未悗留洞鐃� 認鐃緒申的鐃緒申鐃春ワ申鐃緒申鐃宿￥申鐃潤タ認鐃緒申的鐃緒申鐃緒申
LLM鐃祝わ申覺逸申鐃緒申鐃銃逸申鐃緒申團鐃緒申侫鐃緒申奪鐃緒申鵐姐鐃緒申鐃塾実刻申評鐃緒申
鐃緒申ChatMusician 鐃塾￥申鐃緒申鐃緒申鐃緒申
LLM鐃叔駕申鐃准わ申鐃旬種申的鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申ChatMusician
鐃駿ワ申鐃夙ワ申鐃縮子駕申離鐃緒申鐃夙誌申鐃緒申妊鐃緒申鐃術わ申鐃緒申鐃緒申鐃緒申椒鐃獣ワ申鐃緒申鐃准ワ申鐃緒申鐃緒申鐃緒申惱鐃緒申鐃緒申鐃緒申鐃�
鐃粛間わ申多鐃緒申鐃緒申鐃緒申LLM鐃緒申同鐃緒申鐃緒申鐃緒申鐃粛間と醐申鐃緒申鐃叔ルが鐃緒申文鐃熟の醐申鐃緒申分鐃緒申
links


鐃緒申鐃緒申鐃�

geolocation , tweet , mapping 鐃緒申鐃駿件申鐃駿わ申鐃緒申tweet鐃薯検削申鐃緒申鐃殉わ申 updates news aggregator

talking summarization
singletone

blogchart


フィードメーター - 研究開発




<% for ( var i = 0; i < 7; i++ ) { %> <% } %>
<%= wdays[i] %>
<% for ( var i = 0; i < cal.length; i++ ) { %> <% for ( var j = 0; j < cal[i].length; j++) { %> <% } %> <% } %>
0) { %> id="calendar-60183-day-<%= cal[i][j]%>"<% } %>><%= cal[i][j] %>
QR鐃緒申鐃緒申鐃緒申
QRコード
Recent Comments
<%==comments[n].author%>
<% } %>

'); label.html('\ 鐃初イ鐃瞬ドワ申鐃瞬ワ申鐃緒申鐃叔は刻申鐃緒申離僉鐃緒申鐃緒申淵薀わ申鐃緒申鐃緒申鐃緒申測鐃緒申里鐃緒申瓮�鐃獣ワ申鐃緒申(cookie)鐃緒申鐃緒申僂鐃緒申討鐃緒申泙鐃緒申鐃�
\ 鐃緒申鐃塾バナ￥申鐃緒申鐃縦わ申鐃暑か鐃緒申鐃緒申鐃緒申鐃渋鰹申鐃緒申襪鰹申箸妊鐃緒申奪鐃緒申鐃緒申了鐃緒申僂鐃叔э申鐃緒申鐃緒申鐃緒申鐃緒申鐃緒申鐃塾とわ申鐃緒申鐃銃わ申鐃緒申鐃緒申鐃緒申鐃殉わ申鐃緒申
\ 鐃殉わ申鐃緒申鐃緒申鐃緒申鐃粛わ申鐃緒申鐃述パ￥申鐃夙ナ￥申鐃緒申箸砲鐃緒申鐃緒申鐃緒申鐃緒申亮鐃渋鰹申鐃緒申砲鐃所、鐃緒申鐃獣ワ申鐃緒申鐃塾誌申鐃術わ申鐃緒申鐃緒申鐃緒申襪鰹申箸鐃叔わ申鐃殉わ申鐃緒申
\ 鐃旬細わ申鐃初イ鐃瞬ドワ申鐃緒申鐃術居申鐃緒申鬚干鐃叔э申鐃緒申鐃緒申鐃緒申鐃緒申鐃�\ '); banner.append(label); var closeButton = $('