Getting　access　to　labeled　datasets　in　certain　sensitive　application　domains　can　be　challenging.　Hence,　one　may　resort　to　transfer　learning　to　transfer　knowledge　learned　from　a　source　domain　with　sufficient　labeled　data　to　a　target　domain　with　limited　labeled　data.　However,　most　existing　transfer　learning　techniques　only　focus　on　one-way　transfer　which　may　not　benefit　the　source　domain.　In　addition,　there　is　the　risk　of　a　malicious　adversary　corrupting　a　number　of　domains,　which　can　consequently　result　in　inaccurate　prediction　or　privacy　leakage.　In　this　paper,　we　construct　a　secure　and　Verifiable　collaborative　Transfer　Learning　scheme,　VerifyTL,　to　support　two-way　transfer　learning　over　potentially　untrusted　datasets　by　improving　knowledge　transfer　from　a　target　domain　to　a　source　domain.　Furthermore,　we　equip　VerifyTL　with　a　secure　and　verifiable　transfer　unit　employing　SPDZ　computation　to　provide　privacy　guarantee　and　verification　in　the　multi-domain　setting.　Thus,　VerifyTL　is　secure　against　malicious　adversary　that　can　compromise　up　to　$n-1$　out　of　$n$　data　domains.　We　analyze　the　security　of　VerifyTL　and　evaluate　its　performance　over　four　real-world　datasets.　Experimental　results　show　that　VerifyTL　achieves　significant　performance　gains　over　existing　secure　learning　schemes.　IEEE