№1, 2019

Rasim M. Alguliyev, Irada Y. Alakbarova

The social credit system is aimed at the assessment of citizens based on big data representing their daily activities. The social credit score represents the labor activities of citizens and their behavior in society and is important for making the right decisions in e-government in socio-economic and political spheres. The issues such as collecting, cleaning, structuring and analyzing personal data, which is constantly increasing and updating, are important for the assessment of social credit score. Here, information security issues, applied methods and algorithms require accuracy, and any mistakes can lead to the loss of trust and reputation of citizens. Based on this, the article analyzes existing problems in the process of personal data acquisition for the assessment of social credit score. Moreover, the article identifies important issues to be solved to ensure security of personal data and the trust between e-government and a citizen (pp.3-13).

Keywords:social credit score, e-government, personal data, tracking devices, Internet of things, big data, digital footprint, information security.
  • Nasser T., Tariq R.S. Big Data Challenges // Computer Engineering and Information Technology, 2015, vol. 4, no. 3., pp. 1–6.
  • Hatton C. China social credit: Beijing sets up huge system, 2015, http://www.bbc.com/news/world-asia-china-34592186.
  • Pohlmann M. George orwell in China – digitization as a method of total social control, 2018, https://heigos.hypotheses.org/8546
  • Fərdi məlumatlar haqqında Azərbaycan Respublikasinin Qanunu, http://www.e-qanun.az/ framework/19675
  • “İnformasiya, informasiyalaşdırma və informasiyanın mühafizəsi haqqında” Azərbaycan Respublikasının Qanunu, http://e-qanun.gov.az/framework/3525
  • Parkinson B., Millard D.E., O'Hara K., Giordano R. The digitally extended self: A lexicological analysis of personal data // Information Science, 2018, vol. 44, issue 4, pp. 552–565.
  • Botsman R. Big data meets Big Brother as China moves to rate its citizens, 2017, https://www.wired.co.uk/article/chinese-government-social-credit-score-rivacy-invasion
  • Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995, https://eur-lex.europa.eu/legal-content/en/TXT/?uri=CELEX%3A31995L0046
  • Directive 97/66/Ec of the European Parliament and of the Council 15 December 1997, https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX:31997L0066
  • European Convention on Human Rights, http://www.echr.coe.int/Documents/Convention_ ENG.pdf
  • Back M. D., Stopfer J. M., Vazire S., Gaddis S., Schmukle S. C., Egloff B., Gosling S. D. Facebook profiles reflect actual personality, not self-idealization // Psychological Science, 2010, vol. 21, no. 3, pp. 372–374.
  • ZiegeldorfH., Morchon O.G., Wehrle K. Privacy in the Internet of Things: Threats and Challenges // Security and Communication Networks, 2015, vol. 7, no. 12, pp. 1–14.
  • Personal data empowerment. Time for a fairer data deal?, 2015, The National Association of Citizens Advice Bureaux, 56 p.
  • Thomas H. Big Data in Big Companies. Davenport and SAS Institute Inc. 2013, 31 p.
  • Глущенко Н. Слишком большие данные: сколько информации хранится в интернете? 2017, https://ain.ua/special/skolko-vesit-internet/
  • Harris The tyranny of algorithms is part of our lives: soon they could rate everything we do, 2018, https://www.theguardian.com/commentisfree/2018/mar/05/algorithms-rate-credit-scores-finances-data
  • Данченок Л. А. Маркетинг в социальных медиа. Интернет-маркетинговые коммуникации. Учебное пособие. СПб.: Питер, 2013, 288 с.
  • Deakin M., Al Waer H. From intelligent to smart cities // Intelligent Buildings International, 2011, vol. 3, no. 3, pp. 140–152.
  • O’Hara K., Tuffield M.M.,Shadbolt N. Lifelogging: Privacy and empowerment with memories for life // Identity in the Information Society, 2008, vol. 1, issue 1, pp. 155–172.
  • Azucar D., Marengo D., Settanni M. Predicting the Big 5 personality traits from digital footprints on social media: A meta-analysis // Personality and Individual Differences, 2018, 124, pp. 150–159.
  • Duus R., Cooray M., Page N.C. Exploring Human-Tech Hybridity at the Intersection of Extended Cognition and Distributed Agency: A Focus on Self-Tracking Devices // Frontiers in Psychology, 2018, vol. 9, no. 1432,
  • Gangi R.R., Rajesh N.B., Sudhakar N.P., Raviteja B., Rammohanarao K., Tracking objects using rfid and wireless sensor networks // International Journal Of Engineering Science & Advanced Technology, 2012, vol. 2, issue 3, pp. 513 – 517.
  • Redondi, Chirico M., Borsani L., Cesana M., Tagliasacchi M. An integrated system based on wireless sensor networks for patient monitoring, localization and tracking // Ad Hoc Networks, 2013, vol. 11, issue 1, pp. 39–53.
  • Massobrio R., Nesmachnow S., Tchernykh A., Avetisyan A., Radchenko G., Towards a Cloud Computing Paradigm for Big Data Analysis in Smart Cities // Programming and Computer Software, 2018, vol. 44, 3, pp. 181–189.
  • Əliquliyev R.M., Mahmudov R.Ş. Əşyaların İnterneti: mahiyyəti, imkanları və problemləri // İnformasiya cəmiyyəti problemləri, №2(4), 2011, s. 29–40.
  • Интернет вещей: Новые перспективы для людей с инвалидностью, 2015, Публикации и доклады G3ict, 22 с., http://www.unic.ru/sites/default/_Web_UNIC _G3ictRep_IoT_2015_RUS.pdf
  • Алексеев В., Модули Bluetooth, Wi-Fi и NFC производства u-blox для «Интернета вещей», Часть 2 // Беспроводные технологии, 2015, №3, с. 23–24.
  • Əliquliyev R.M., İmamverdiyev Y.N. E-dövlətin informasiya təhlükəsizliyi: aktual tədqiqat istiqamətləri // İnformasiya cəmiyyəti problemləri, 2010, №1, s. 3–13.