# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 2 Related Works  
In this section, we provide an overview and summary of the related works in the PPIL field, with a detailed comparison presented in Table 1.  
Research on PPIL dates back to 2014. PriWFL [10] used Paillier encryption [21] to protect the privacy of participants in a client-server setting, but this design was later proven to be insecure [18]. In the same year, a design combining Paillier encryption and oblivious transfer (OT) [22; 23] leveraged location information from other users for positioning [11]. However, in their design, the computational burden required of users was too high. Three subsequent works combined Paillier encryption with garbled circuits (GC) [24; 25] for PPIL design. [PERSON] et al. [18] also discussed the weaknesses of some previous designs, but these were only analyzed and discussed theoretically, without experimental validation of their proposed solution's performance. On the other hand, [PERSON] et al. [19] focused on discussing methods to reduce the representation bit length of RSS values through techniques such as quantization to lower the overhead. While theoretical performance improvements were achieved, they too did not conduct experimental validation. [PERSON] et al. [16] further employed optimizations such as precomputation by users and servers, as well as Paillier ciphertext packing, to reduce costs. They also experimentally validated the specific performance of their approach. However, the computational overhead of homomorphic encryption and the communication overhead introduced by garbled circuits still resulted in overall performance that was less than ideal. [PERSON] et al. [15] extended the homomorphic encryption-based algorithm to further include the DGK cryptosystem [26]. However, the performance overhead of their solution was substantial, with users bearing a heavy burden of homomorphic computations. As a result, even on small datasets, queries took tens of seconds to complete. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **Technique** & **Query Time** & **Communication** & **Security** \\ \hline
[10] & Paillier & Slow & Middle & \(\times\) \\
[11] & Paillier + OT & Middle & Middle & \(\checkmark\) \\
[18] & Paillier + GC & N/A & High & \(\checkmark\) \\
[19] & Paillier + GC & N/A & High & \(\checkmark\) \\
[15] & Paillier + DGK & Slow & Middle & \(\checkmark\) \\
[13] & GC + OT & Fast & High & \(\checkmark\) \\
[12] & Paillier & Slow & Middle & \(\checkmark\) \\
[16] & Paillier + GC & Slow & High & \(\checkmark\) \\ Delta sharing + & & Fast & High & \(\checkmark\) \\ GC & & & & \\
[20] & IPE & Slow & Middle & \(\checkmark\) \\
[17] & IPE + LSH & Fast & High & \(\times\) Access Pattern \\ \(\mathrm{Sillcom}_{3R}\) & RSS + FSS & Middle & Low & \(\checkmark\) \\ \(\mathrm{Sillcom}_{trd}\) & RSS + FSS & Fast & Low & \(\checkmark\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of existing PPIL schemes. In the Security column, \(\times\) indicates that the scheme has security flaws, while \(\checkmark\) denotes that the scheme is secure. [PERSON] [13] implemented the first practical design under the outsourcing model. They based their solution on the ABY framework [27], utilizing garbled circuits and oblivious transfer, and validated the performance of the scheme across multiple distance metrics. Through various combinations of MPC optimization techniques, their solution achieved a feasible user query time. However, the computational servers incurred huge communication overhead, which significantly impacted the throughput and scalability of their scheme. [PERSON] et al. [12] further considered stronger security settings and extended the Paillier-based design to achieve malicious security. However, similar to previous works, the computational overhead became the performance bottleneck of their design. FAPRIL [14] integrates Delta sharing [28] and garbled circuits to design a two-party PPIL scheme.