Recognizing　computer　users＇　handedness　provides　important　clues　for　profiling　computer　criminals　in　digital　forensic　analysis.　Existing　technologies　for　handedness　recognition　have　two　main　problems　that　limit　their　applicability　in　the　scenario　of　digital　crimes:　they　can　be　intrusive,　and　they　require　costly　equipment.　Our　solution　is　to　infer　users＇　handedness　by　analyzing　keystroke-typing　behavior.　Field　studies　are　first　conducted　to　gather　users＇　keystroke-typing　data　during　their　interaction　with　computers.　Timing　features　are　extracted　to　characterize　users＇　typing　rhythms,　and　the　correlation　between　keystroke　features　and　handedness　is　analyzed.　Classification　techniques　are　then　developed　for　handedness　recognition.　Experimental　results　show　that　the　handedness　could　be　efficiently　and　accurately　inferred　from　users＇　keystroke-typing　behavior,　with　recognition　rates　expressed　by　the　Area　Under　the　ROC　Curve　(AUC)　of　87.75%.　To　our　knowledge,　this　is　the　first　work　that　infers　users＇　handedness　based　on　their　keystroke-typing　biometric　during　interaction　with　computers,　without　dedicated　and　explicit　actions　that　require　attention　from　users.　©　2016　IEEE.