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+are+susceptible+to+memorizing+training+data%252C+including+a+large+amount+of+private+or+copyright-protected+content.+To+safeguard+the+right+to+be+forgotten+(RTBF)%252C+machine+unlearning+has+emerged+as+a+promising+method+for+LMs+to+efficiently+forget+sensitive+training+content+and+mitigate+knowledge+leakage+risks.+However%252C+despite+its+good+intentions%252C+could+the+unlearning+mechanism+be+counterproductive%253F+In+this+paper%252C+we+propose+the+Textual+Unlearning+Leakage+Attack+(TULA)%252C+where+an+adversary+can+infer+information+about+the+unlearned+data+only+by+accessing+the+models+before+and+after+unlearning.+Furthermore%252C+we+present+variants+of+TULA+in+both+black-box+and+white-box+scenarios.+Through+various+experimental+results%252C+we+critically+demonstrate+that+machine+unlearning+amplifies+the+risk+of+knowledge+leakage+from+LMs.+Specifically%252C+TULA+can+increase+an+adversary%27s+ability+to+infer+membership+information+about+the+unlearned+data+by+more+than+20%2525+in+black-box+scenario.+Moreover%252C+TULA+can+even+reconstruct+the+unlearned+data+directly+with+more+than+60%2525+accuracy+with+white-box+access.+Our+work+is+the+first+to+reveal+that+machine+unlearning+in+LMs+can+inversely+create+greater+knowledge+risks+and+inspire+the+development+of+more+secure+unlearning+mechanisms.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.6a081e92782990d08bb8e7fcd6c1ff7a%3frik%3dmWLiQaPL3PPSWA%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Neural+Network+Unlearning&FORM=IRPRST&ck=2F77731BCE1CD213A8B68D3D9C67D9FC&selectedIndex=14&itb=0)
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