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Make matchmaking work with cases as prefs
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8960b9a69c
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GPG key ID:
CBBBE7371E81C4EA
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@ -6,9 +6,12 @@ from Compiler.util import *
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from Compiler.oram import OptimalORAM
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from Compiler.library import for_range, do_while, time, if_, print_ln, crash, print_str
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from Compiler.library import for_range, do_while, time, if_, print_ln, crash, print_str, break_loop
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from Compiler.gs import OMatrix, OStack
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# Optimizations
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program.use_edabit(True)
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class Matchmaker:
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"""
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@ -27,19 +30,22 @@ class Matchmaker:
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self.unpaired.append(patient, for_real)
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def request_therapist(self, patient, therapist, for_real):
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(requested_therapist,), free = self.paired_patients.read(therapist)
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experience = self.t_exps[therapist][0]
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(old_patient,), free = self.paired_patients.read(therapist) # patient paired to therapist
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paired = 1 - free
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rank_patient = self.t_exps[therapist][patient]
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(rank_requested_therapist,), worst_therapist = self.t_exps[therapist].read(paired*requested_therapist)
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leaving = self.int_type(rank_patient) < self.int_type(rank_requested_therapist)
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if self.M < self.N:
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leaving = 1 - (1 - leaving) * (1 - worst_therapist)
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print_str('therapist: %s, patient: %s, requested therapist: %s, worst therapist: %s, ',
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*(x.reveal() for x in (therapist, patient, requested_therapist, worst_therapist)))
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print_ln('rank patient: %s, rank requested therapist: %s, paired: %s, leaving: %s',
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rank_patient = self.p_cases[patient][0]
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rank_old_patient = self.p_cases[old_patient][0] * paired
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matches_exp = self.int_type(rank_patient) == self.int_type(experience)
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same_as_old = self.int_type(rank_patient) != self.int_type(rank_old_patient)
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leaving = paired * matches_exp * same_as_old
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print_str('therapist: %s, patient: %s, old patient: %s, ', # worst therapist: %s, ',
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*(x.reveal() for x in (therapist, patient, old_patient))) # , worst_therapist)))
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print_ln('rank patient: %s, rank old patient: %s, paired: %s, leaving: %s',
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*(x.reveal() for x in
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(rank_patient, rank_requested_therapist, paired, leaving)))
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self.unpair(requested_therapist, therapist, paired * leaving * for_real)
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(rank_patient, rank_old_patient, paired, leaving)))
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self.unpair(old_patient, therapist, paired * leaving * for_real)
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self.pair(patient, therapist, (1 - (paired * (1 - leaving))) * for_real)
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self.unpaired.append(patient, paired * (1 - leaving) * for_real)
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@ -50,6 +56,7 @@ class Matchmaker:
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else:
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loop = for_range(n_loops)
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init_rounds = n_loops / self.M
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self.paired_therapists = \
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self.oram_type(self.N, entry_size=log2(self.N),
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init_rounds=0, value_type=self.basic_type)
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@ -60,7 +67,7 @@ class Matchmaker:
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self.oram_type(self.N, entry_size=log2(self.N),
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init_rounds=0, value_type=self.basic_type)
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self.unpaired = OStack(self.N, oram_type=self.oram_type,
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int_type=self.int_type)
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int_type=self.int_type)
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@for_range(init_rounds)
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def _(i):
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@ -72,40 +79,54 @@ class Matchmaker:
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rounds.iadd(1)
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time()
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patient = self.unpaired.pop()
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pref = self.int_type(request_therapist[patient])
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if self.M < self.N and n_loops is None:
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@if_((pref == self.M).reveal())
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pref = self.int_type(p_cases[patient][0]) # patient suffers from pref
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therapist = types.MemValue(self.int_type(0))
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# Get index of next free therapist who has experience with pref
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@for_range(self.N)
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def _(i):
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(_,), free = self.paired_patients.read(i)
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@if_(((pref == t_exps[i][0]) * free).reveal())
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def _():
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therapist.write(self.int_type(i))
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break_loop()
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@if_((i == self.N).reveal())
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def _():
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print_ln('run out of acceptable therapists')
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crash()
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request_therapist[patient] = pref + 1
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self.request_therapist(patient, self.p_cases[patient][pref], True)
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request_therapist[patient] = therapist.read()
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self.request_therapist(patient, therapist.read(), True)
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print_ln('patient: %s, pref: %s, left: %s',
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*(x.reveal() for x in (patient, pref, self.unpaired.size)))
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return types.regint((self.unpaired.size > 0).reveal())
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print_ln('%s rounds', rounds)
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print_ln('\nPRINTING PAIRS\n')
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@for_range(init_rounds)
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def _(i):
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types.cint(i).print_reg('ther')
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self.paired_patients[i].reveal().print_reg('pati')
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print_ln('patient %s : therapist %s', i, self.paired_therapists[i].reveal())
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#print_ln('therapist %s : patient %s', i, self.paired_patients[i].reveal())
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#types.cint(i).print_reg('ther')
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#self.paired_patients[i].reveal().print_reg('pati')
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def __init__(self, N, p_cases, t_exps, M=1, reverse=False,
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def __init__(self, p_cases, t_exps, N, M=None, reverse=False,
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oram_type=OptimalORAM, int_type=types.sint):
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self.N = N
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self.M = M
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self.M = N if M is None else M
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self.p_cases = p_cases
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self.t_exps = t_exps
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self.reverse = reverse
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self.oram_type = oram_type
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self.int_type = int_type
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self.basic_type = int_type.basic_type
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print('match', N, M)
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# Constants
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PLAYERS = 3
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MATCHING_SIZE = 10
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MATCHING_SIZE = 50
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p_shares = Matrix(rows=PLAYERS, columns=MATCHING_SIZE, value_type=types.sint)
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t_shares = Matrix(rows=PLAYERS, columns=MATCHING_SIZE, value_type=types.sint)
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@ -114,28 +135,18 @@ t_shares = Matrix(rows=PLAYERS, columns=MATCHING_SIZE, value_type=types.sint)
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# The matrix is ordered as m[row:player][col:share]
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@for_range(PLAYERS)
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def _(i):
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p_index = MemValue(cint(0))
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t_index = MemValue(cint(0))
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@for_range(2 * MATCHING_SIZE)
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def _(j):
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index = j % MATCHING_SIZE
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typ = sint.get_input_from(i)
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@do_while
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def _():
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try:
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typ = cint.get_input_from(i)
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@if_((typ == -100).reveal())
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def _():
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p_shares[i][index] = sint.get_input_from(i)
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@if_e(typ == -100)
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def _():
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p_shares[i][p_index.read()] = sint.get_input_from(i)
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p_index.iadd(1)
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@else_
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def _():
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@if_(typ == -200)
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def _():
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t_shares[i][t_index.read()] = sint.get_input_from(i)
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t_index.iadd(1)
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return 1
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except Exception:
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return 0
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@if_((typ == -200).reveal())
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def _():
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t_shares[i][index] = sint.get_input_from(i)
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# Add entire column together to reveal secret-shared input
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p_cases = OMatrix(N=MATCHING_SIZE, M=1, oram_type=OptimalORAM, int_type=types.sint)
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@ -144,7 +155,10 @@ t_exps = OMatrix(N=MATCHING_SIZE, M=1, oram_type=OptimalORAM, int_type=types.sin
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@for_range(MATCHING_SIZE)
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def _(i):
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p_cases[i][0] = sum(p_shares.get_column(i))
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print_ln('p_case %s: %s', i, p_cases[i][0].reveal())
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t_exps[i][0] = sum(t_shares.get_column(i))
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print_ln('t_exp %s: %s', i, t_exps[i][0].reveal())
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mm = Matchmaker(MATCHING_SIZE, p_cases, t_exps)
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# Run algorithm
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mm = Matchmaker(p_cases, t_exps, N=MATCHING_SIZE, M=1)
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mm.match()
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