The future of privacy and security

10

October

2018

5/5 (4)

Nowadays, there is a rising concern for our privacy on the world wide web. We tend to become more suspicious of every piece of information we have to provide.  A few years ago, this was not the case as we all trusted the antivirus programs which we used on our computers. As recent data breaches have had a profound impact on the way we treat our data, customers demand more security from companies. It may even seem that the only way to keep a secret is to keep it offline on a piece of paper.

If you want to do some online shopping and eventually buy something, you have to prove that you have enough money to buy the product. In other words, you have to provide the website your bank details in order to make them confident that you have enough money to buy the product. But what if you could prove that your capable of buying the product without having to reveal all your bank accounts’ details. This is where zero-knowledge proof comes into play. The zero-knowledge protocol allows someone to prove that a statement is true without revealing any further information to the one verifying it (Chase, Ganesh et al. 2016).

The question that rises is: How can this technique help us to improve our online privacy and security online? First of all, the zero-knowledge proof is based on three requirements (Bootle, Cerulli et al. 2015).

  • Completeness: The prover can convince the verifier if a statement is true.
  • Soundness: The prover cannot convince the verifier if the statement is false.
  • Zero-Knowledge: The verifier will gain any knowledge about the statement.

For example, blockchain transactions are visible to everyone in that particular network. The zero-knowledge protocol enables the same transaction without giving away data about the sender, quantity and asset. In other words, the other players in the network only know that the transaction is valid without the data being revealed (Wang, Kogan 2018).

In my opinion, the zero-knowledge proof is a promising tool to enable better privacy and data protection in the future. Furthermore, the possibilities when fully operational online are endless.

 

 

 

 

Bootle, J., Cerulli, A., Chaidos, P. And Groth, J., 2015. Efficient Zero-Knowledge Proof Systems. Foundations Of Security Analysis And Design Viii. Springer, Pp. 1-31.

Chase, M., Ganesh, C. and Mohassel, P., 2016. Efficient zero-knowledge proof of algebraic and non-algebraic statements with applications to privacy preserving credentials, Annual Cryptology Conference 2016, Springer, pp. 499-530.

Wang, Y. And Kogan, A., 2018. Designing confidentiality-preserving Blockchain-based transaction processing systems. International Journal of Accounting Information Systems, 30, pp. 1-18.

 

 

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Artificial Intelligence in personnel selection.

30

September

2018

5/5 (9)

Nowadays, a human resource manager spends a lot of time selecting the right staff for their organization. To see if the candidates fit the job description, sufficient and trustworthy information needs to be evaluated (Dipboye 2014). Managers increasingly question the credibility of the provided information by candidates on their CVs (Weiss, Feldman 2006). Artificial intelligence can come into play assisting human resource managers and there is even a possibility of making them obsolete in the future.

Unilever, a transnational consumer goods company, already started experimenting with staff selection assisted by artificial intelligence making resumes superfluous. The company starts by scanning LinkedIn profile data using an algorithm which drops half of the candidates (Thibodaux 2017). Subsequently, several games using artificial intelligence to assess and match candidates to the company have to be played. Finally, less than the top third submits a video interview focusing on business challenges (Gee 2017). All these steps combined will accelerate the human resource pre-selection phase without the intervention of humans. Furthermore, according to Unilever the hiring process has become more accurate as 80 percent of the applicants in the final round are offered a job (Gee 2017).

Although these developments in the selection procedure may seem revolutionary in a sense that the process becomes more accurate. Of course, the other side of the coin should also be considered. The use of artificial intelligence in the process also raises some questions concerning the legitimacy of the selection. Artificial intelligence is not entirely unbiased as it is basing its decisions on data provided by humans. Besides, is it ethical to exclude people based on a decision made by artificial intelligence? As more people are selected through this process the companies behind it should measure the effects. Think of cases where too many people with the same background are selected or it influences company performance negatively. As artificial intelligence has already made its way to the pre-selection phase of personnel in a big company like Unilever, people should start thinking of the consequences of using it.

Dipboye, R.L., 2014. The role of communication in intuitive and analytical employee selection, Meeting the challenge of human resource management: A communication perspective 2014, Routledge New York, NY, pp. 40-51.
GEE, K., 2017. In Unilever’s Radical Hiring Experiment, Resumes Are Out, Algorithms Are In. Dow Jones Institutional News.
THIBODAUX And WANDA, June 28th, 2017-last update, Unilever Is Ditching Resumes in Favor of Algorithm-Based Sorting. Available: https://www-inc-com.eur.idm.oclc.org/wanda-thibodeaux/unilever-is-ditching-resumes-in-favor-of-algorithm-based-sortingunilever-is-di.html?cid=search [27-09-, 2018].
Weiss, B. and Feldman, R.S., 2006. Looking good and lying to do it: Deception as an impression management strategy in job interviews. Journal of Applied Social Psychology, 36(4), pp. 1070-1086.

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