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Security and privacy of customer data as an element creating the image of the company

Żywiołek Justyna, Trigo Antonio, Rosak-Szyrocka Joanna, Khan Asghar

Management Systems in Production Engineering

2022

nr 2 (30)

156--162

COVID-19, mobility, socio-social changes have transferred to the world of social media communication, purchasing activities, the use of services. Corporate social media has been created to support clients in using various services, give them the possibility of easy communication without time and local barriers. Unfortunately, they still very rarely take into account the security and privacy of customers. Considering that the purpose of this article is to investigate the impact of social media on the company's image, it should be remembered that this image also works for the security and privacy of customer data. Data leaks or their sale are not welcomed by customers. The results of empirical research show that the safety, simplicity and variety of services offered on social media have a significant impact on the perceived quality, which in turn positively affects the reputation. The authors proposed a methodology based on the Kano model and customer satisfaction in order to examine the declared needs and undefined desires and divide them into different groups with different impacts on consumer satisfaction. The interview participants were employees of 10 randomly selected companies using social media to conduct sales or service activities. 5,000 people from Poland, Portugal and Germany participated in the study. 4,894 correctly completed questionnaires were received.

A Locality Sensitive Hashing Filter for Encrypted Vector Databases

Kawamoto J.

Fundamenta Informaticae

2015

Vol. 137, nr 2

291--304

We introduce a filtering methodology based on locality-sensitive hashing (LSH) and whitening transformation to reduce candidate tuples between which encrypted vector databases (EVDBs) must compute similarity for query processing. The LSH hashing methodology is efficient for estimating similarities between two vectors. It hashes a vector space using randomly chosen vectors. We can filter vectors that are less similar to the querying vectors by recording which hashed space each vector belongs to. However, if vectors in EVDBs are found locally, then most vectors are in the same hashed space, so the filter will not work. Because we can treat those cases using whitening transformation to distribute the vectors broadly, our proposed filtering methodology will work effectively on any vector space. We also show that our filter reduces the server’s query processing cost.