PP-DDP: a privacy-preserving outsourcing framework for solving the double digest problem.

BACKGROUND: As one of the fundamental problems in bioinformatics, the double digest problem (DDP) focuses on reordering genetic fragments in a proper sequence. Although many algorithms for dealing with the DDP problem were proposed during the past decades, it is believed that solving DDP is still very time-consuming work due to the strongly NP-completeness of DDP. However, none of these algorithms consider the privacy issue of the DDP data that contains critical business interests and is collected with days or even months of gel-electrophoresis experiments. Thus, the DDP data owners are reluctant to deploy the task of solving DDP over cloud. RESULTS: Our main motivation in this paper is to design a secure outsourcing computation framework for solving the DDP problem. We at first propose a privacy-preserving outsourcing framework for handling the DDP problem by using a cloud server; Then, to enable the cloud server to solve the DDP instances over ciphertexts, an order-preserving homomorphic index scheme (OPHI) is tailored from an order-preserving encryption scheme published at CCS 2012; And finally, our previous work on solving DDP problem, a quantum inspired genetic algorithm (QIGA), is merged into our outsourcing framework, with the supporting of the proposed OPHI scheme. Moreover, after the execution of QIGA at the cloud server side, the optimal solution, i.e. two mapping sequences, would be transferred publicly to the data owner. Security analysis shows that from these sequences, none can learn any information about the original DDP data. Performance analysis shows that the communication cost and the computational workload for both the client side and the server side are reasonable. In particular, our experiments show that PP-DDP can find optional solutions with a high success rate towards typical test DDP instances and random DDP instances, and PP-DDP takes less running time than DDmap, SK05 and GM12, while keeping the privacy of the original DDP data. CONCLUSION: The proposed outsourcing framework, PP-DDP, is secure and effective for solving the DDP problem.

Comparison of Clinical Effects between Modified and Conventional Delta-Shaped Anastomosis in Totally Laparoscopic Distal Gastrectomy: A Retrospective Study.

Background: This study evaluates the feasibility, safety, and clinical results of the self-pulling and latter transected delta-shaped anastomosis (Delta SPLT) in totally laparoscopic distal gastrectomy (TLDG) for gastric cancer. Methods: We performed a retrospective study of 66 patients with gastric cancer undergoing laparoscopic distal gastrectomy with Billroth-I anastomosis from May 2017 to December 2018 in Zhoushan Hospital. TLDG with Delta SPLT was carried out in 26 patients (Group 1), and TLDG with conventional delta-shaped anastomosis (DA) was performed in 40 patients (Group 2). Statistical analysis was conducted to compare clinical data between the two groups. Results: All patients successfully underwent TLDG. There were no significant differences between the two groups in terms of demographic indicators, operation time, anastomosis time, intraoperative blood loss, number of lymph nodes harvested, and resection margin (all P > .05). The gastrointestinal functional evaluation index (first flatus, first liquid/semigeneral diet foods, and out-of-bed mobilization) and hospital stay did not differ between the two groups, but the mean hospital charges were significantly lower in Group 1 than in Group 2 (P < .05). No difference was observed in the overall postoperative complication rate (P > .05). However, Group 1 had a lower incidence of complications associated with anastomosis (3.8%, versus 7.5% in Group 2; P = .016). Conclusions: Delta SPLT is potentially a safe, feasible, and reproducible reconstruction option for TLDG, and was superior to conventional DA in terms of hospital charges and complications related to anastomosis.

MIStore: a Blockchain-Based Medical Insurance Storage System.

Currently, blockchain technology, which is decentralized and may provide tamper-resistance to recorded data, is experiencing exponential growth in industry and research. In this paper, we propose the MIStore, a blockchain-based medical insurance storage system. Due to blockchain's the property of tamper-resistance, MIStore may provide a high-credibility to users. In a basic instance of the system, there are a hospital, patient, insurance company and n servers. Specifically, the hospital performs a (t, n)-threshold MIStore protocol among the n servers. For the protocol, any node of the blockchain may join the protocol to be a server if the node and the hospital wish. Patient's spending data is stored by the hospital in the blockchain and is protected by the n servers. Any t servers may help the insurance company to obtain a sum of a part of the patient's spending data, which servers can perform homomorphic computations on. However, the n servers cannot learn anything from the patient's spending data, which recorded in the blockchain, forever as long as more than n - t servers are honest. Besides, because most of verifications are performed by record-nodes and all related data is stored at the blockchain, thus the insurance company, servers and the hospital only need small memory and CPU. Finally, we deploy the MIStore on the Ethererum blockchain and give the corresponding performance evaluation.