A Privacy-preserving Computation Offloading Method Based on k-Anonymity
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摘要: 针对移动边缘计算(MEC)中用户的卸载任务及卸载频率可能使用户被攻击者锁定的问题，该文提出一种基于k-匿名的隐私保护计算卸载方法。首先，该方法基于用户间卸载任务及其卸载频率的差异性，提出隐私约束并建立基于卸载频率的隐私保护计算卸载模型；然后，提出基于模拟退火的隐私保护计算卸载算法(PCOSA)求得最优的k-匿名分组结果和组内各任务的隐私约束频率；最后，在卸载过程中改变用户原始卸载频率满足隐私约束，最小化终端能耗。仿真结果表明，PCOSA算法能找出用户所处MEC节点下与用户卸载表现最相近的k个用户形成匿名集，有效保护了所有用户隐私。Abstract: Users’ offloading tasks and offloading frequencies in Mobile Edge Computing(MEC) may cause users to be locked out. A privacy-preserving computation offloading method based on k-anonymity is proposed in this paper. Firstly, based on the differences between offloading tasks and their frequencies, privacy constraint is proposed to establish a privacy-preserving computation offloading model based on offloading frequency; Then, a Privacy-preserving Computation Offloading algorithm based on Simulated Annealing (PCOSA) is utilized to obtain the optimal k-anonymous groups and the privacy constraint frequency of each task; Finally, the user’s original offloading frequencies are changed to meet the privacy constraint while minimizing terminal energy consumption. Simulation results validate that the PCOSA can find out k users with the closest offloading performance to form anonymous sets, which protects effectively the privacy of all users.
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表 1 PCOSA I 算法流程
输入：用户数N，各任务的卸载概率${P_{\rm{U}}}$，分组大小k，隐私保护
门限${\theta _p}$，隐私保护阈值$\theta $输出：分组结果及组内各卸载任务的平均频率 (1) 根据式(3)计算所有k个用户分组的代价 (2) 将N个用户随机排列，按顺序每k个为一组作为初始解${\rm{now}}$ (3) While $T > {T_{\min}}$ (4) ${\rm{new}} \leftarrow $随机交换可行解${\rm{now}}$中两个用户位置 (5) $\varDelta \leftarrow {\rm{cost(now)} } - {\rm{cost(new)} }$ (6) If $\varDelta \ge 0$ (7) ${\rm{now}} \leftarrow {\rm{new}}$ (8) Else (9) ${\rm{now}} \leftarrow $以${{\rm{e}}^{\varDelta /T} }$的概率将${\rm{new}}$赋值 (10) $T = T \cdot \lambda $ (11) End While (12) 通过将最优解${\rm{now}}$按顺序每k个为一组得到最优分组$X$ 
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表 2 PCOSA II 算法流程
初始化：卸载总次数${N_{\rm{O}}}$=0； 任务累积卸载次数${{N} } = 0$ (1) 观察用户当前的$h_i^2{\rm{(} }t{\rm{)} },i = 1,2, ···, {N_{ {\rm{AP} } } }$和$\xi (t)$； (2) 计算最优的${f^*}{\rm{(}}t{\rm{)}},\;E_{\rm{L}}^*{\rm{(}}t{\rm{)}},\;{p^*}{\rm{(}}t{\rm{)}},\;E_{\rm{M}}^*{\rm{(}}t{\rm{)}}$； (3) If ${p^*}{\rm{(}}t{\rm{)}} > {p_{{\rm{max}}}}$无法卸载 (4) If ${f^*}{\rm{(}}t{\rm{) > }}{f_{{\rm{max}}}}$ 丢弃任务，$E{\rm{(}}t{\rm{) = }}{E_0}$； (5) Else 本地处理，$E{\rm{(}}t{\rm{) = }}E_{\rm{L}}^*{\rm{(}}t{\rm{)}}$； (6) Else (7) 根据式(4)计算可卸载任务数$N_m^{\rm{P}}$ (8) flag初始化为1，根据式(5)为flag赋值0； (9) If flag==1 (10) 执行卸载，$E{\rm{(}}t{\rm{) = }}E_{\rm{M}}^*{\rm{(}}t{\rm{)}}$ (11) $N_{\rm{O}}^{}$和${N_m}$均累加1； (12) Else (13) 本地处理，$E{\rm{(}}t{\rm{) = }}E_{\rm{L}}^*{\rm{(}}t{\rm{)}}$； (14) 根据式(6)生成假任务
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表 3 模型参数设置
参数 取值 单位时隙长度${l_s}$ 1 ms 信道增益$h_i^2$服从指数分布，均值$\overline {h_i^2} $ –90 dB 信道增益$h_i^2$服从指数分布，量化步长$\delta _{{h^2}}^i$ $\overline {h_k^2} /1000$ 上行链路带宽${W_i}$ 1 MHz 噪声功率密度$N_0^i$ ${10^{ - 19}}\;{\rm{W/Hz}}$ CPU最大频率${f_{\max}}$ 2 GHz 能耗系数$\kappa $ ${10^{ - 28}}$ 终端天线最大发射功率${p_{\max}}$ 1 W 任务大小b ${10^3}$ bit 处理1 bit数据所需CPU循环数$\beta $ ${10^3}$ 任务截止时间$\xi {\rm{(}}t{\rm{)}}$服从均匀分布 $\left\{ {0.01{l_s},0.02{l_s}, ··· ,{l_s}} \right\}$ 任务丢弃代价${E_0}$ $10 \cdot \kappa \beta bf_{{\rm{max}}}^2$
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