alue, a feasible solution can take the round value if it is fractional, in [15], they also take the same approximation. When Lk is very large as the most real-world cases, the approximation has minimal impact to the original problem. 8.3 Computation Efficiency Maximization with Active Eavesdropper algorithm. Specifically, the entropy function has first-order Taylor series expansion at (x,y) = (xo,yo) where (xo,yo) is the differentiable point. It is easy to verify that given (xo,yo),f(x,y) becomes an affine form, which is convex. In optimization problems, we solve for (x,y) with given feasible point (xo,yo) first, then in the next round, (xo,yo) becomes the previous round's (x,y). The process will continue until converges. SCA-based approach works well in the iterative algorithms and received much attention recently. In the following, to simplify notations, we first transform (8.4d) to equation sets below: (8.5a) (8.5b) (8.5c) where TK and Nk are auxiliary variables. It is easy to verify that the transformation is equiv- alent. Furthermore, (8.5b) should be transformed according to and f(Nk,tk) that mentioned above. 8.3.2 Objective Function Next, the objective function needs to be converted to the convex form as well. Currently, it is the summation of the fractional functions (represent each UE's computation efficiency). Traditional Dinkelbach's method cannot be applied directly since it can only deal with one fractional function. Instead, following [92], we can generalize Dinkelbach's algorithm to tackle one fractional function to multiple ones by a simple transformation. (8.6a) (8.6b) C2 : East. (8.6c) (8.6d) (8.6e) (8.6f) C6 : (8.5a)-(8.5c). (8.6g) Toward Green MEC Offloading with Security Enhancement Here, we let for notational simplicity. Bk is the auxiliary variable. (8.7a) {Pk},{T}},{Nk} s.t. B (8.7b) (8.6c), (8.6e), (8.6f), (8.5a), (8.5c) (8.7c) P3 can be solved with the following Lemma. Lemma 8.1 For solution of P3, there must exist the Karush- Kuhn-Tucker (KKT) condition of the following problem for Nk= 