In　this　paper,　we　propose　a　privacy-preserving　clinical　decision　support　system　using　Naive　Bayesian　(NB)　classifier,　hereafter　referred　to　as　Peneus,　designed　for　the　outsourced　cloud　computing　environment.　Peneus　allows　one　to　use　patient　health　information　to　train　the　NB　classifier　privately,　which　can　then　be　used　to　predict　a　patient＇s　(undiagnosed)　disease　based　on　his/her　symptoms　in　a　single　communication　round.　Specifically,　we　design　secure　Single　Instruction　Multiple　Data　(SIMD)　integer　circuits　using　the　fully　homomorphic　encryption　scheme,　which　can　greatly　increase　the　performance　compared　with　the　original　secure　integer　circuit.　Then,　we　present　a　privacy-preserving　historical　Personal　Health　Information　(PHI)　aggregation　protocol　to　allow　different　PHI　sources　to　be　securely　aggregated　without　the　risk　of　compromising　the　privacy　of　individual　data　owner.　Also,　secure　NB　classifier　is　constructed　to　achieve　secure　disease　prediction　in　the　cloud　without　the　help　of　an　additional　non-colluding　computation　server.　We　then　demonstrate　that　Peneus　achieves　the　goal　of　patient　health　status　monitoring　without　privacy　leakage　to　unauthorized　parties,　as　well　as　the　utility　and　the　efficiency　of　Peneus　using　simulations　and　analysis.